Effect of climatological factors on ground-layer vegetation: a case study to assess and protect the fire-prone grasslands of Odisha, India
Bibliographic record
Abstract
Vegetation diversity and the effect of fire on grassland communities in Odisha were evaluated, along with phenological behaviour in response to climatic factors. This study noted 3100 individuals from 63 grassland taxa under 12 families and 44 genera. Of these, 22 grass species and 11 non-grass species were found in burned grassland sites, while 13 grass species and 17 non-grass species were observed in unburned sites. About 81% of the examined individuals flowered in burned sites compared to 35% in unburned sites. The phytosociological investigation reveals that Imperata cylindrica, Cynodon dactylon, Mimosa pudica, and Dichanthium annulatum thrived and showed dominance after a severe fire. Phenological analyses revealed that flower initiation positively correlates with available nitrogen (r = 0.8, p < 0.01) and rainfall (r = 0.6, p < 0.01). However, a negative correlation was found between fruit senescence and soil moisture (r = -0.8, p < 0.01). Principal component analysis revealed that rainfall and available nitrogen are the main factors explaining phenological divergence in sampling sites. This case study on the interaction between phenological data and fire may help to conserve fire-resistant species and develop a conservation strategy for grassland taxa in fire-prone regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".