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About this Context Graph:

total nodes:  extend
Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

Angry agriculture is the most fundamental and ancient activity that propelled human civilization as we know it today. Before agriculture. Yeah, there were people there were modern humans, but they formed groups of hunters and gatherers, and they didn't build cities they didn't build empires. So, agriculture is perceived by many as a very conservative activity. However, it has been over the time characterized by a rate of innovation that was necessary at in order to be able to feed the populations. And of course, the rate of population was statistically speaking exactly equal with the amount of calories that our culture could produce at a given time. Because if you had more people, and not enough calories, there will be starvation and death. This kind of cycle of overshooting population that will then go back to the sustainable level has characterized human civilization for 1000s of years, 10s of 1000s of years,

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and it was invisible to most unless they were the one starving. And now we have a global interconnected world. And we know when there is food crisis, somewhere. And hopefully, we intervene. We help, we prevent famine and starvation to kill people. Actually, agriculture now is powerful enough in its ability to create the output edible calories that when there is famine. It is not due to lack of food. It is due to insufficient logistics, or active prevention of the population being fed by civil war and conflict or corruption. However, the way that agriculture, arrived to the point where it is today. must change. The impact of agriculture, on the environment must be ameliorated must be lightened, the pressure that humanity, puts on the environment is not compatible with our future. I was lucky to participate in the opening meeting of a new think that the same thing farm to fork. That was born to catalyze the rate of innovation, and the rate of technology adoption in agriculture.

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Especially, following the European Union. Common Agricultural Policy, that is being put in place.

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highlighting sustainability and highlighting biodiversity. And actually, very explicitly putting in place incentives and objectives to reach this in the way that every kind of activity in agriculture and food production is going to be influenced by it over the course of the next five years, the policies that the European Union puts in place, matter worldwide because of how big a market it represents both in terms of how it produces food to be consumed inside, or for export or its import of food from from outside. And the size of the market also makes it so that the producers of technologies and components in Europe, or outside of Europe that serve the European market are able to leverage that size in order to achieve both their financial goals, as well as to be able to develop new products, new services that otherwise wouldn't be available. So, just a few examples of the things that that we are seeing that are not only on the horizon, but are already in agricultural practice today.

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The ability to to use

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the ability to plow at a depth, that is much shallower than before. In order not to disturb and not to diminish the fertility of the field, the simultaneous laying of tubes underground at a depth, that is deeper than than the plowing depth. In order to irrigate the field with the right quantities of water enriched with the kind of nutrients that the particular plant needs exactly where it is needed. Because of both satellite based and drone based monitoring of plant growth through data analysis and integration, based on multispectral imaging. The opportunity is really enormous already the existing practices can improve farm productivity, double digits, 10 20%, and more wild diminishing drastically. The use of water. The. Wait a the pressure of food production on the fertility of terrain. And, of course, quite substantially diminishing goes to the use of chemicals that that are the traditional fertilizers or pesticides, at which contributed. Importantly, in our ability to increase the

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level of food production we have today but where they are now being labeled or recognized as undesirable beyond a certain necessary level. The next stages of agricultural revolution again will come from technology. Imagine vertical farming. That can be above ground or below ground it, that is proportional to the volume of the form, rather than the area of the field available. The availability of plant based meat substitutes that are being adopted and appreciated by consumers already. Or actually, a cultivated meat of animal origin obtained by cell cultures, rather than growing, an entire traditional animal. These are just two examples, but also advanced techniques like CRISPR in our ability to intervene and improve the basic crops and and the basic ingredients of our food chain, there are obstacles. For example, in Italy. Today, you cannot apply, pesticides, via drones, Which practice would be able to diminish the quantity, put out quite radically because flying the drone, even if

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applied small cap quantities very precisely because it flies, a meter or two meters above ground is equated to blasting the crops. With traditional spraying from planes, and that is prohibited. And as a consequence, the use of drones is also prohibited,

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other examples where the regulations are not aligning the incentives in actually delivering the desired outcome, largely large quantities of healthy food in great variety, with as little environmental pressure as possible across not only Europe, but the world. And then of course the most radical of food production practices are going to be on the Moon and Mars and the asteroid belt environments where well you cannot grow corn or feed a cow with the corn that you grew. You cannot waste water you cannot have almost any of the things that we are accustomed to have on Earth. The total, not only radical but total sustainability practices that moon colonies and Mars colonies are going to have will be not only advanced, but very valuable and Earth, agriculture, is going to be profoundly influenced by those practices that Mars will be happy to sell to earth and consult and advise on their implementation in the best possible way, the agricultural sector is something that I am not very familiar

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with, except eating food. And for me, it was a pleasure to start this collaboration with the think tank farm to fork. And I am looking forward to the next meeting guy I'm looking forward to interact both with the providers of these exciting, interesting existing already being deployed agricultural technologies, as well as with the farmers that are the heart and and really the passion that they put in their work of what they produce, and we eat. Thank you very much and see you in the next episode of the context.

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Language Processing Settings:

language logic: stop words:
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse structure:
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Network Structure Insights
mind-viral immunity:
The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

Influence Distribution
Topics Nodes in Top Topic Components Nodes in Top Comp
Nodes Av Degree Density Weighed Betweenness

Narrative Influence Propagation:
The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

Degree Distribution:
  calculate & show   ?
(based on kolmogorov-smirnov test) ?   switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
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Main Topical Groups:

please, add your data to display the stats...
+     full stats   ?     show categories

The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
Most Influential Elements:
please, add your data to display the stats...
+     Reveal Non-obvious   ?

We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Network Structure:
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

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Action Advice:
Structural Gap
(ask a research question that would link these two topics):
Reveal the Gap   Generate a Question   ?
A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

Latent Topical Brokers
(less visible terms that link important topics):

These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

Emerging Keywords

Evolution of Topics
(number of occurrences per text segment) ?
The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.

Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

Main Topics
(according to Latent Dirichlet Allocation):

LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):

We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

Sentiment Analysis

positive: | negative: | neutral:
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We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

The approach is based on AFINN and Emoji Sentiment Ranking

Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.

Keyword Relations Analysis:

please, select the node(s) on the graph see their connections...
+   ⤓ download CSV   ?

Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
Top Relations / Bigrams
(both directions):

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).

Text Statistics:
Word Count Unique Lemmas Characters Lemmas Density
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
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