Dynamic assessment of agro-industrial sector efficiency and productivity changes among G20 nations
Bibliographic record
Abstract
In this study, the Group of 20 (G20; excluding EU economies) were selected as the research objects, and the dynamic network slacks-based model (SBM) was used to evaluate the impact of carbon dioxide (CO 2 ) emissions and forested area on the efficiency and productivity of the industrial and agricultural sectors from 2011 to 2015. Empirical results showed that: (1) The efficiency of the industrial sector was superior to that of the agricultural sector among the G20 countries. Argentina, Australia, Indonesia, Saudi Arabia, South Africa, Turkey, the UK, and the US maintained the best industrial sector efficiency values, falling on the efficiency boundary, whereas Argentina, Brazil, Canada, France, Indonesia, South Korea, Russia, and the US had the best agricultural sector efficiency values. (2) Argentina, Indonesia, and the US had the best overall efficiency value of G20 countries. Saudi Arabia (0.0303), China (0.2721), and the UK (0.2809) had the lowest efficiency values. (3) Only France and Germany had higher than average total factor productivity, while Indonesia and Saudi Arabia had declining industrial and agricultural sector productivity. (4) The proportion of forested area (546.02%) was the most important variable to be improved due to the influence of desert topography, followed by the proportion of agricultural output values (60.86%) and the proportion of industrial output values (38.02%) in some countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".