Bibliographic record
Abstract
Canada’s federal carbon tax is currently inconsistently applied to agricultural fuels. Depending on the type of fuel and its use, farmers may be eligible for a full exemption from the carbon tax, a partial exemption or they may face the full amount of the tax. As an industry, agriculture has comparable sector-level emissions and trade flows as industrial sectors that are eligible to receive carbon pricing support through participation in the federal output-based pricing system (OBPS) and comparable provincial programs Agriculture is currently excluded from these programs, however, as it is characterized by thousands of small producers, the vast majority of which do not exceed the required minimum emissions thresholds. Looking ahead, agriculture should be evaluated to determine whether it meets the emissions intensity and trade exposure thresholds for voluntary participation in federal and provincial OBPS programs. If these thresholds are met then this provides a strong argument for extending carbon pricing support to all sources of combustion emissions in agriculture. Any future support should be offered through a mechanism that maintains the full incentive of the carbon tax. The preferred options are either a lump-sum rebate that is independent of fuel use and emissions, or an output-based rebate system specific to agriculture.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 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".