Bibliographic record
Abstract
TABLES 1 Canadian and u.s.tariffs on selected forest products as of i January 1989 / 11 2 Increase in average delivered wood costs: 1983 estimates / 64 3 Short-run impact on the BC economy of 15 per cent u.s.import and Canadian export tariffs on softwood lumber / 67 4 Long-run impact on the BC economy of 15 per cent u.s.import and Canadian export tariffs on softwood lumber / 69 5 TFL allowable cut and harvest by forest region / 91 6 ISA allowable cut, commitment, and harvest by forest region / 92 7 Relative levels of regional softwood lumber production costs, factor prices, productivity, and the exchange rate / 100 8 Growth of regional softwood lumber production costs, factor prices, productivity, and the exchange rate /102 9 Product mix of the Canadian pulp and paper industry /106 10 Canadian pulp and paper industry shipments / 107 11 Comparison of the Canadian pulp and paper industry in 1900 and 1920 / 108 12 Relative Canada-u.s.production costs of other paper and paperboard as affected by differences of factor prices and productivity, and the exchange rate /115 13 Growth of Canada-u.s.production costs of other paper and paperboard due to the effect of differences of factor prices and productivity, and the exchange rate /117 14 u.s.pulp and paper tariffs /120 15 Canadian pulp and paper tariffs in percentages /122
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.888 | 0.774 |
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".