Migrating like a herd of cats – climate change and emerging forests in British Columbia
Bibliographic record
Abstract
We combine climate preferences of tree species with probable changes in insect, disease, fire and other abiotic factors to describe probable changes in distribution of tree species in British Columbia. Predictions of what British Columbia’s forests will become are rife with uncertainty from three major sources: predicting climate, predicting tree species’ responses and predicting changes in factors modifying the trees’ responses (e.g., pathogens, fires). Challenges in predicting climate result because climate projection models differ and downscaling climate is difficult, particularly where weather stations are sparse. Challenges in predicting responses of individual tree species to climate result because species will be competing under a climate regime we have not seen before and they have not experienced before. That challenge is increased by the differential response of pathogens and effects of changes in fire frequency. We first examine responses of individual species, then consider implications for broad regional forests. Despite the uncertainty, some trends are more likely than others. We present our estimates of the relative species composition of future forests in British Columbia.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".