Bibliographic record
Abstract
The summer sky is now too often the colour of turmeric. Forest fire smoke, blown in from hundreds of kilometers away, gets stuck like too-wide nets between buildings. Edmonton, Alberta, where I have lived for nearly twenty years is a city on the edge of the Canadian Boreal Forest. It is the largest part of the Taiga, the second largest forest in the world that stretches across Canada and continues across Iceland, Norway, Sweden, Finland, Russia, Mongolia, and Japan. In Canada it covers 270 million hectares between flat prairie and the treeless arctic tundra. The Boreal Forest is the homeland of more than 600 Indigenous communities, most of the known fossil fuel reserves in Canada, it stores more than 208 billion tons of carbon (11% of the world’s total), and along with the Amazon Rainforest is the second of the two great lungs of the world.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.219 | 0.225 |
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".