Global Weirding in British Columbia: Climate Change and the Habitat of Terrestrial Vertebrates
Bibliographic record
Abstract
The authors summarize the distribution of terrestrial vertebrates of British Columbia across major habitat types and present empirical and projected effects of global weirding within two particularly vulnerable habitats—alpine and wetland. Global weirding embraces all phenomena associated with climate change: increases in average temperatures, heat waves, cold spells, floods, droughts, hurricanes, blizzards, plant and animal die-offs, population explosions, new animal migration patterns, plus dramatic regional differences. Current data suggest that many alpine species will be lost to changes in habitat wrought by climate, particularly increases in average temperatures. For many wetlands, particularly in the central and southern interior of the province, the basic issue is simple—the incoming water is decreasing and the outgoing water (evaporation) is increasing. The authors illustrate three approaches to projecting trends in wetland habitat, elaborating on the “drying index” approach, in which they have most confidence. For wetland species, they say management will struggle with the concept of a real-world triage—allocating conservation efforts where they are most likely to succeed and have the most benefit. They conclude that several conservation approaches for wetland species will face the difficulty of allocating water between needs of these species and of humans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".