Bibliographic record
Abstract
British Columbia (BC) has been described as a sea of mountains unsuitable for growing almost everything except trees (Barber 1995). Native Americans were the first loggers harvesting trees for shelter, transportation and for ceremonial purposes. When settlers came from Europe in the mid-1800s they brought with them their Old-world preference for open fields, food crops and livestock. Forests were viewed as an obstacle to be cleared away, but their removal was a losing struggle against stubborn stumps, relentless undergrowth and thin soils. Eventually, the people realised that lumber was the most valuable crop and harvesting began in earnest in the late I 860s. By 1900 forest harvesting was the leading industry in the province and it has never relinquished that position (Barber 1995). The sheer scale of the forests dominated my first impressions of BC, the vast forest industry, vitally important to every community. Nearly everyone I met had some connection with forestry. Almost every town has a sawmill or pulp mill, many of the roads warn of logging trucks, rivers are full of logs waiting to be pulled up to the sawmills. Even the houses in Vancouver have huge Douglas fir (Pseudotsuga menziesii) spruce trees in their gardens; the streets downtown are lined with trees. Most of the building construction is with wood. Forests, trees and wood are an intimate part of the culture, life-style and community of BC.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".