On the Urgency of Implementing the Scientific Outlook on Development from the Perspective of the Agricultural Resources Condition
Bibliographic record
Abstract
In order to further the people' understanding of the urgency of implementing the scientific outlook on development and fulfill people' consciousness of scientific outlook on development,this paper,with comparative method,compares the possession condition of partial natural resources of world six great countries(country area 7.5 million) and also the difference between developmental cost of China and world average.The result indicates China has twice population of the total of other five great countries and has the greatest population density while China possesses the least fertile land,water and forest resources and also the differences are very large.In China,the per capita fertile land possession is 0.21 hm2,which is only 0.8 % of Australia(25.6 hm2);the per capita water resources is 2 292 m2,which is only 2.3 % of Canada(98 462 m2);the per capita forest resources is 0.11 hm2,which is just 1.3% of Russia(8.24 m2.It is easy to find that China has high development cost and resource consumption is very surprising.Therefore,it is very urgent for China to strengthen the education of national situation,strictly enforce saving policy and scientifically make the national decisions to avoid the resource wasting.Meanwhile,the strategy of invigorating the country through science and education should be energetically carried ou,recycling economy be developed and the high energy-consumption or release-exhaustion and low quality or profit agricultural and industrial enterprises be banned.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".