Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Canadian government has just laid out a new plan to reduce green house gas emissions. A central pillar of the plan is to decrease the greenhouse gas (GHG) intensity of production in a number of key sectors rather than impose a direct cap on emissions. This paper considers the proposal in light of historical trends in these sectors. To assess the possible impact of the policy on the performance of targeted sectors, we decompose the change in emission intensities into composition and technique effects using a divisia index approach. Our results demonstrate that the proposed policy pushes Canadian businesses into reductions in emission intensities that they have not previously accomplished. It is not business as usual. Depending on how credits are given for past emission reductions, total sectoral emissions could switch from positive growth to negative growth although it would take much longer to reduce emissions back to 1990 levels. Further, though these sectors have seen a decrease in emission intensities, the policy would accelerate these reductions significantly. Mention the UK in this paragraph as it comes as a surprise in the next paragraph However, the data also shows that Canadian and UK businesses have had only limited success in improving their techniques of production in terms of reducing GHG emissions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 it