Bibliographic record
Abstract
Forest is a ecological system with multi-benefits.Since it plays a crucial part in global ecological safety and sustainable development of economic society and contributes great to human beings, forest should be emphasized, protected and developed. This passage analyses reasons why forest ecological environment in the west deteriorates, and points out that the major reason is that the need of providing against old age in the countryside leads to the excessively rapid increase of population which consequently result in overcultivation and overcut of forest. As for such cause, the passage put up ways of restraining further deterioration of ecological environment and specific counter measures of improving and constituting the ecological environment of forest in the west. Key words: forest ecology, population Resume: La foret est un systeme rentable. Il joue un role cle dans la securite ecologique et le developpement durable economique et social du monde et apporte de grandes contributions a l’humanite. Il faut la bien proteger et developper. L’article present etudie les raisons de la degradation de l’environnement ecologique de la foret, dont la principale est l’exploitation excessive de la foret due a la croissance rapide de la population rurale. L’auteur propose encore des contre-mesures pour contenir la degradation de l’environnement ecologique, et des mesures concretes visant a traiter et proteger l’environnement. Mots-cles: environnement ecologique de la foret, controle de la population 摘要：森林是多效益的生態係統，其在全球生態安全和經濟社會可持續發展中起著關鍵性作用，對人類的貢獻巨大，應予以積極保護和發展。本文分析了森林生態環境惡化的原因，主要因素是農村養老需要導致人口過快增長從而引起過度農墾、樵採等；並且提出了抑制森林生態環境進一步惡化的對策，以及治理和建設森林生態環境的具體措施。 關鍵詞：森林生態環境；人口制約
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".