Bibliographic record
Abstract
The article reviews two new multi purpose snow removal machines that have changed both the dynamic and size of snow removal teams at many busy airports. Toronto's Pearson International Airport, by using one of the new multi purpose equipment, has set the bar for minimum runway down time for snow clearing - 10 to 15 minutes for a runway, a parallel taxiway and two high speed turnoffs, plus chemicals, sand application and inspection. One of the keys to Toronto's speed-cleaning is equipment that is custom fit for the job at hand, rather than compromise equipment. At BWI airport, crews are experimenting in-situ with the available new equipment. Complimenting the issue for BWI is that its runways intersect, which means that both runways must be shut down temporarily to clean the intersection, and crews take about 30 minutes to clear the 150 foot wide runway. The airport is investigating how the multi purpose machines will help accelerate the clearing. Despite the heavy up-front investment for the new machines, expectations are that the equipment costs can be recouped in savings to the airlines in one year or less.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.135 | 0.047 |
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".