Identification of the environmental factors which drive the botanical and functional composition of permanent grasslands
Bibliographic record
Abstract
SUMMARY Managed grasslands provide environmental and agronomic services that can be predicted from the botanical and functional composition of the vegetation. These are influenced by management, edaphic and climatic factors. The present report set out to estimate and analyse the relative importance of management, soil and climate factors on botanical and functional characteristics of grassland vegetation. A set of 178 French grasslands having a large pedoclimatic and management gradient was selected, and information collected on botanical composition, pedoclimatic factors and management. Six vegetation characteristics were considered: two botanical (floristic composition and species dominance) and four functional (proportion of entomophilous species, number of oligotrophic species, leaf dry matter content and date of flowering). First, the links between the characteristics of the vegetation were analysed to check for any redundancy among them; all were kept. Second, it was demonstrated that botanical and functional characteristics were not driven by the same factors: functional composition was characterized by management, edaphic and climatic factors, whereas botanical composition was influenced mainly by climatic and edaphic factors plus other factors. Interactions between factors also have to be taken into consideration to predict botanical and functional composition of grasslands. Functional and botanical characteristics of vegetation help to predict ecosystem services delivered by grasslands and may be used in combination.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".