Diversity and functional groups dynamics affected by drought and fire in Patagonian grasslands
Bibliographic record
Abstract
During 1998–1999 a severe drought occurred in northwestern Patagonia that provoked an extensive wildfire. We monitored vegetation cover and the soil seed bank to study the diversity and functional group gap dynamics in burned and unburned sites. Species were grouped into 3 functional groups: forbs, fugitive species, and annual grasses. Post-drought vegetation recovered quickly due to a rainy spring in the second year but decreased after a dry and warm spring in the third year. These patterns underline the close relationship that exists between phenological phases and meteorological variables. Drought decreased richness but did not affect the presence of stress-tolerant species, whereas fire increased richness by allowing the establishment of fugitive species. Species in the fugitive functional group may be fire adapted and depend on seed accumulation in the seed bank (storage effect) to coexist with other gap species. Forbs exhibited their highest vegetation cover and seed bank density in the unburned site. Global climate change suggests an increase in the frequency and amplitude of El Niño/Southern Oscillation phenomena that, in northwestern Patagonia, are related to the occurrence of drought, fire, and changes in vegetation dynamics.
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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 | 0.000 | 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".