Regional and ecological patterns in interior Douglas-fir climate–growth relationships in British Columbia, Canada
Bibliographic record
Abstract
How climate change will affect tree growth across species’ geographic and climatic ranges remains a critical knowledge gap. Tree-ring data were analyzed from 33 interior Douglas-fir ( Pseudotsuga menziesii var. glauca (Beissn.) Franco) stands spanning wide geographic and climatic conditions in the interior of British Columbia to gain insights into how within-species growth responses to climate can vary based on local environmental conditions over a broad climatic and geographic range, including populations growing at the species’ range and climatic margins. Populations growing in relatively warm and dry climates had growth patterns correlated mostly with annual precipitation, whereas populations growing in high-elevation wet and cold climates had growth patterns correlated with snowfall, winter and annual temperatures, and ocean–atmosphere climate systems. Populations growing at climatic extremes (e.g., coldest, driest, warmest) in each study region had the strongest responses to climate. Projected climate change may negatively influence Douglas-fir productivity across most of its range, and populations growing near the species’ climatic limits may provide early and strong indications of future responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".