Data for: Species-rich boreal forests grew more and suffered less mortality than species-poor forests under the environmental change of the past half-century (Hisano et al. 2019/Ecology Letters)
Bibliographic record
Abstract
Climate and other global environmental changes are major threats to ecosystem functioning and biodiversity. However, the importance of plant diversity in mitigating the responses of functioning of natural ecosystems to long-term environmental change remains unclear. Using inventory data of boreal forests of western Canada from 1958-2011, we found that aboveground biomass growth increased over time in species-rich forests but decreased in species-poor forests, and importantly, aboveground biomass loss from tree mortality was smaller in species-rich than species-poor forests. A further analysis indicated that growth of species-rich (but not species-poor) forests was statistically positively associated with rising CO2, and that mortality in species-poor forests increased more as climate moisture availability decreased than it did in species-rich forests. In contrast, growth decreased and mortality increased as the climate warmed regardless of species diversity. Our results suggest that promoting high tree diversity may help reduce the climate and environmental change vulnerability of boreal forests.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.043 |
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".