Bibliographic record
Abstract
Rapid urbanization and the increasing interest in sustainable land use have urged decision makersto consider replacing aboveground urban freeways with underground road tunnels. However,tunnelling major urban auto infrastructure would increase its vulnerability to natural hazards suchas fire, earthquake, and flood. Despite the significant consequences, the risks of these hazards onroad tunnels have not been thoroughly investigated. This study develops a digital inventory of aroad tunnel network proposed for Montreal using ArcGIS and conducts a case study to assess thetunnel risk under confined fire events. The highways between Anjou and Decarie feature hightraffic volumes and are considered to be buried underground and replaced with alternative roadtunnels. The ArcGIS data inventory is developed by positioning the tunnels along existing highwayroutes, and assembling and pre-processing a database for surrounding soil and rock profiles. Therelative risk scores of different tunnel segments under fire exposures due to possible vehiclecombustions and collisions are assigned based on their distances to the nearest (1) egress pointsfor human evacuation and (2) fire stations for fire suppression. As such, a relative fire risk map isdeveloped in ArcGIS for sampling locations at 100 m of spacing along the tunnel line.Subsequently, a new fire station is proposed to be placed near the high-risk site, showing itscapability to diminish the peak risk score from 100 to 60. The developed digital tunnel-soil/rockinventory and the fire-risk case study provide a viable pathway for end users to assess the spatialdistribution of hazard risk, pinpoint high-risk sites, and interactively quantify the effectiveness ofdifferent risk mitigation strategies
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".