Bibliographic record
Abstract
Recent research in British Columbia, Canada, finds that one reason why moose and other ungulates use highways and byways in their seasonal migrations may be to access roadside deposits, or mineral But when these features exist near roadways, they entice moose to use habitat dangerously close to the motoring public. This article details new methods which try to reduce animal-vehicle collisions by deactivating roadside deposits. Researchers at the University of Northern British Columbia and in California are coordinating research efforts to determine how to reduce moose-car collisions. Their study objectives are to (1) define strategies that will result in increased motorist safety, (2) reduce material damage claims, and (3) conserve the animal resource. It plans to diminish the attractiveness of roadside licks in order to reduce moose activity near roadways. One deactivation technique involves excavating a lick site and backfilling the area with materials unattractive to moose. Reinforcing fabric materials placed over the site is another option that could inhibit access to the soils and water and serve as a base for placing sod and planting unpalatable plant species. Likewise, covering the site with boulders or asphalt debris might deter moose visit. Still another technique is to spread a layer of lime or cement over the lick site and mix into the wet soil to creat a 6- to 24-inch layer that would cure and become a hard surface material, thus reducing the attractivenes of the area to ungulates. Rerouting site hydrology and drying up the lick might reduce it attractiveness since moose are attracted to wet licks. Campaigns to reduce animal-auto collisions should consider all possibilities and should consider what an animal is doing in a corridor. If a moose is there to forage, countermeasures should concentrate on diminishing its foraging base. In areas where there are frequent moose-auto collisions, installing new signage and posting reduced speed limits would alert motorists about potential threats and offer interim solutions until better deactivation techniques can be implemented in those areas. A project team from the University of Northern British Columbia will begin field testing in the summer of 2006, and in 2008 it expects to recommend the most effective techniques to the Canadian Ministry of Transportation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".