Zagotavljanje migracijskih koridorjev za prostoživeče živali na območju železniške infrastrukture
Bibliographic record
Abstract
Rail transport is considered to be more environmentally friendly, economical and socially acceptable than other types of land transport, especially when compared to road transport. However, it can adversely affect wildlife by creating barriers to their movement, commonly known as the “barrier effect”, and by directly increasing mortality due to collisions with trains. Therefore, it is crucial to plan and implement mitigating measures to ensure ecological connectivity and reduce wildlife mortality caused by rail traffic. The primary focus of such measures should be on preventing animals from accessing and lingering on railway tracks, since trains typically cannot avoid collisions. Measures that effectively reduce the number of collisions without exacerbating the barrier effect are particularly desirable. In this review article, we focused on measures that have been proposed or implemented in railway infrastructure. Additionally, we examine recent research exploring the feasibility of alternative mitigation measures, such as warning systems tested in Canada and Sweden. The second part of the paper presents a proposal for a protocol designed to ensure migration corridors and minimize barrier effects. The protocol was developed based on relevant literature and previous studies, as well as our own experience in planning and implementing monitoring measures to reduce wildlife mortality (with a focus on ungulates) on roads, highways and freeways. It also incorporates an analysis of collisions between wildlife and trains on the Slovenian railways network during a selected five-year period.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".