Data from: Divergent temporal trends of net biomass change in western Canadian boreal forests
Bibliographic record
Abstract
1. Forests play a strong role in the global carbon cycle by absorbing atmospheric carbon dioxide through increasing forest biomass. Understanding temporal trends of forest net aboveground biomass change (ΔAGB) can help infer how forest carbon sequestration responds to on-going climate changes. Despite wide spatial variation in the long-term average of climate moisture availability (CMIaverage) across forest ecosystems, temporal trends of ΔAGB associated with CMIaverage remains unclear. 2. We tested the hypothesis that the extent of negative impacts of climate change on ΔAGB would decrease with CMIaverage using the data from permanent sample plots of varying ages from 17 to 210 years, monitored from 1958 to 2011 in western boreal forests of Canada. 3. We found that ΔAGB on average increased with CMIaverage. Temporally, ΔAGB declined sharply between 1958 and 2011 in plots with low CMIaverage owing to increased biomass loss from mortality accompanied by little growth gain, whereas ΔAGB changed little in plots with high CMIaverage. The temporal decrease of ΔAGB in drier areas was attributable to its negative responses to warming-induced temporal decreases in climate moisture availability. 4. Synthesis. Our results indicate that large-scale changes in forest carbon functioning associated with climate change depend on the long-term average of climate moisture availability. Our finding suggests a possible retreat of boreal biome at the drier distribution limits with predicted declines in water availability in the 21st century.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".