Bibliographic record
Abstract
With an area of 1,700,000 km, Quebec offers a climate with major temperature variations, given its geographic features. In addition, there are special conditions: the St. Lawrence River, which runs through marine clay deposits recognized as sensitive; many lakes, reservoirs and hydroelectric dams; several airports in isolated regions; a road network estimated at 320,000 km. The Ministere des Transports du Quebec (MTQ) manages and maintains 30,400km of this network (highway system), including some 10,000 structures (bridges, overpasses and other). Most of the transportation infrastructures were built from 1960 to 1980 on the basis of knowledge which has evolved considerably since then; the transportation infrastructures were designed on the basis of climate scenarios, which today are being reconsidered or modified. The main risks confronting Quebec are: melting of the permafrost in northern Quebec, shoreline erosion in coastal environments along the St. Lawrence and in the Gulf, forest fires, floods due to high-water levels and high tides, landslides, freezing rain and other extreme winter conditions, and transport of heavy equipment and hazardous materials. The MTQ has adopted some risk management tools and is pursuing research and development regarding roads and structures to implement solutions adapted to the Quebec context. In a context of budget restrictions, the MTQ must target and prioritize its interventions wisely and prepare to deal with emerging challenges, generated by climate change or resulting from the interdependence of essential transportation systems. To this effect, integrated risk management, based on objective, reliable and verifiable multidisciplinary data and on rigorous and well-documented methodologies, is still the preferred approach.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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 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".