Bibliographic record
Abstract
RESUME Les changements climatiques sont devenus l’un des enjeux environnementaux principaux des dernieres annees, les emissions de gaz a effet de serre (GES) etant pointees du doigt comme principal coupable. Globalement, les decideurs des politiques tentent depuis un certain temps de reduire les emissions des GES a travers diverses mesures et politiques. Considerant qu’en Amerique du Nord, le domaine du transport compte pour 30% des emissions totales, il est devenu le centre d’attention pour les initiatives de reduction des emissions de GES.----------ABSTRACT Climate change has become one of the most critical environmental concerns of the past decades, with greenhouse gas (GHG) emissions being identified as the main culprit. Globally, policy makers have been trying to reduce GHG emissions through various policies and strategies; given that in North America, transportation accounts for 30% of the total emissions, it has become the focus of attention for GHG reduction initiatives. The first step to implementing a policy or strategy is to estimate its potential impact on emissions; the use of emission models is necessary to assess the potential impact of those initiatives. Since the 70s, many researchers have developed different models, reaching a peak in the number of studies in the 80s. The emission models have evolved since then and have been regularly updated, but still need improvements. Since these models are extremely sensitive to their input datasets and their methods of calibration, failing to provide accurate input datasets or calibration can result in erroneous stimations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".