The Price of Compromise: Why We Should Wind Down Our Forest
Bibliographic record
Abstract
L’industrie forestière canadienne fait face à deux problèmes majeurs. Biologiquement, la liquidation de la forêt boréale naturelle et son remplacement par un nombre limité d’autres essences de même âge poussant dans la même région risquent de réduire la biodiversité et de changer les systèmes climatiques et d’écoule-ment des eaux. Économiquement, le lent taux de croissance de la forêt boréale implique que l’investisse-ment dans le reboisement n’est pas très intéressant. Mais sans reboisement, nous allons manquer de bois dans quelques décennies. Des points de vue biologique et économique nous devrons donc apporter des changements majeurs à notre industrie forestière pour conserver une portion suffisante de la forêt naturelle de façon à s’assurer d’avoir les ressources nécessaires à la régénération à long terme de nos forêts si nos efforts de reboisement échouent. Ceci va comporter un changement vers un approvisionnement en bois de haute qualité et vers des utilisations de la forêt qui permettent de la conserver. Canada’s forest industry faces two major problems. Biologically, the liquidation of the natural boreal forest and its replacement by even-aged stands of a limited number of species poses major risks of reducing biodiversity, and changing climatic and water-flow patterns. Economically, the slow rate of growth of the boreal forest means that any investment in replanting makes little sense, but without replanting we will run out of wood in the next few decades. On both biological and economic grounds, then, we need to make
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".