Bibliographic record
Abstract
Canada's west coast, temperate, old-growth forests include its largest, most commercially valuable, fastest-growing, oldest, and certainly most fought-over forests. They can be divided into three main types: coastal rainforest, coastal subalpine forest, and "rain-shadow" forest. Although there is great variation within each of these broad types, coastal rainforests and subalpine forests share a wet climate and are relatively unimpacted by fire as a stand-replacing disturbance. This allows development of multi-aged, multi-canopy, old-growth forests with large volumes of living and dead wood. These forests are structurally and biologically complex. Coastal rain-shadow forests, on the other hand, have a distinctively drier climate (for the coast), and a history of frequent, low-intensity fires. Although well over half of Canada's original west coast, temperate, old-growth forests remain as old growth, there is great variation ecologically and geographically. In general, the percentage of old-growth forest remaining increases with increasing latitude and elevation. Key words: old growth, old-growth forest, coastal British Columbia, temperate rainforest, protected areas, stand structure
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".