Une mise à jour des taux d'amortissement pour les Comptes canadiens de productivité
Bibliographic record
Abstract
Le present document fournit les estimations mises a jour des taux de depreciation qu?il convient d?utiliser dans les Comptes canadiens de productivite pour calculer le stock de capital et le cout d?usage du capital. Les estimations sont derivees des courbes de depreciation etablies pour un ensemble varie d?actifs en se basant sur les profils des prix de revente et des ages de mise hors service. La methode du maximum de vraisemblance est appliquee pour estimer conjointement les variations de la valeur des actifs au cours de leur vie utile, ainsi que la nature du processus de mise hors service des actifs utilises, afin de produire les taux de depreciation. Cette methode convient mieux que d?autres, car elle produit des estimations dont le biais est plus faible et l?efficacite, plus elevee. Les estimations anterieures, calculees pour la periode allant de 1985 a 2001, sont comparees a celles obtenues pour la periode la plus recente, allant de 2002 a 2010.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".