Bibliographic record
Abstract
1 Introduction 22 Canada’s Climate Change Plan 22.1 Budgeting for CCP Initiatives . . . . . . . . . . . . . . . . . . 33 Reduction of Canadian Greenhouse Gas Emissions 83.1 Key Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 83.1.1 Domestic Permit Trading . . . . . . . . . . . . . . . . 83.1.2 International Permit Trading . . . . . . . . . . . . . . 93.1.3 Hybrid Approaches . . . . . . . . . . . . . . . . . . . 103.2 Regional Analysis of Greenhouse Gas Reductions . . . . . . . 123.2.1 Domestic Permit Trading with Targeted Measures . . 123.2.2 Domestic and International Permit Trading with Tar-geted Measures . . . . . . . . . . . . . . . . . . . . . . 133.3 Provincial Disaggregation . . . . . . . . . . . . . . . . . . . . 144 CREAP Project 145 Data 156 Non-technical Sketch of the CMRT Model 156.1 Energy Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176.2 Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186.3 Issues Raised by Regional Context . . . . . . . . . . . . . . . 187 Illustrative Results 187.1 Qualifications . . . . . . . . . . . . . . . . . . . . . . . . . . . 217.2 Results Summary . . . . . . . . . . . . . . . . . . . . . . . . . 238 Summary 23A CMRT Goods and Sectors 281
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".