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Record W3010969360 · doi:10.1002/essoar.10500819.1

Sustainable Research on World Potassium Resource Trade Based on Complex Network Theory

2019· article· en· W3010969360 on OpenAlex
Rui Kong, Mingyue Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsInternational tradeResource (disambiguation)Distribution (mathematics)Trade barrierFree tradeBusinessOrder (exchange)International economicsEconomicsComputer scienceMathematics

Abstract

fetched live from OpenAlex

As a national strategic resource, trade activities of potassium resources(K) have changed its state of ownership. Futures and spot trading forms in the international market are mainly primary processed ore and deep-processed products. So, K trade can be replaced by the potassium salt trade. Its international trade will affect a country’s strategic resource management, and increase national resource security risks. Thence, it is necessary to study the evolution of international trade in K. This paper has constructed a weighted and directed complex network model of K trade using UN Comtrade 2000-2016 data and analyzed the scale and activity of international trade of K, trade relations, trade flow distribution and the importance of countries. By analyzing the international trade data of K in 224 countries, it is found that trade is active year by year and K is becoming more significant. From network density and diameter, resource allocation is more convenient. The network cluster is growing. It shows that some countries form trade groups. The correlation coefficient of degrees is less than 0, indicating that the trade relationship between small and major trade countries is enhancing. And, the reciprocal coefficient is between 0.1 and 0.35, showing that the trade order is poor. Moreover, it presents a state that the greater the country’s degree, the smaller the difference in trade flow distribution. So, resource flows in countries with more trade relations can promote a balanced distribution of K. Finally, from countries’ trade influence and hub status, Canada is a leading trade country, and the US, the Russian Federation, China and Brazil are trade-led countries. They are the main source of K flows. China, the Netherlands, the US, France and India are important hubs. So, countries should strengthen bilateral trade relations. So as to ensure the SD of international trade in K, major trading countries should focus on the exploit of K and improve the level of production and processing tech. Countries should also enhance their hub role to facilitate the flow of K. China, as a large country in agriculture and K demand, should increase self-sufficiency to reduce import dependence risks. Besides, attention should be paid to market changes in major trading countries and to reducing risks by adding the number of trade countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.318
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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