Effets de la fertilisation sur la production, la valeur nutritive et la diversité floristique d'une prairie de fauche en marais charentais
Bibliographic record
Abstract
Understanding how fertilisation affects the flora found in natural marsh grasslands along the Atlantic coast is an important part of establishing environmental standards for agricultural systems. We conducted a seven-year experiment in a permanent grassland in the Charente department of France. A variety of fertilization treatments were applied to our study plots (i.e., mineral vs. organic fertilisation; use of nitrogen fertilisers vs. nitrogen fertilisers containing P, K, or S; and different quantities of nitrogen fertilisers [0, 60, or 100 units of N/ha/year]). We found that although nitrogen fertilisation did not improve feed value (crude protein content and digestibility), it did improve forage yield as of the first year of the experiment. It also benefited grasses, to the detriment of legumes and sedges. Fertilisation resulted in a minimal loss of species richness and biodiversity exclusively on plots that received high levels of fertilisers (an average of 2 species on plots receiving 100 units of N/ha/year). As soon as the treatments ended, forage yields dropped to control levels. In contrast, floristic changes persisted for 4 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".