Forest‐Wide Growth Rates Stabilize After Experiencing Accelerated Temperature Changes Near an Alaskan Glacier
Bibliographic record
Abstract
Abstract How forests respond to accelerated climate change will affect the terrestrial carbon cycle. To better understand these responses, more examples are needed to assess how tree growth rates react to abrupt changes in growing‐season temperatures. Here we use a natural experiment in which a glacier's fluctuations exposed a temperate rainforest to changes in summer temperatures of similar magnitude to those predicted to occur by 2050. We hypothesized that the onset of glacier‐accentuated temperature trends would act to increase the variance in stand‐level tree growth rates, a proxy for forest net primary productivity. Instead, dendrochronological records reveal that the growth rates of five, co‐occurring conifer species became less synchronous, and this diversification of species responses acted to reduce the variance and to increase the stability of community‐wide growth rates. These results warrant further inquiry into how climate‐induced changes in tree‐growth diversity may help stabilize future ecosystem services like forest carbon storage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".