Bibliographic record
Abstract
The height of the jump-outs should be low enough for the target species to readily jump down to the safe side, or the habitat side, of the fence. At the same time, the jump-outs should be high enough to discourage animals that are on the habitat side of the fence from jumping up into the fenced road corridor. Previous research along US Hwy 93 North in Montana showed that only about 32% of the mule deer and about 7% of the white-tailed deer that appeared on top of the jump-outs, jumped down to safety. For this project, 10 of the jump-outs along US Hwy 93 North were lowered in height and provided with a bar on top. The height of the bars (made from rebar) and their setback from the vertical face of the jump-outs was adjustable and the researchers applied 4 different treatments: 2 different heights (18 and 15 inches) and 3 different setbacks (4, 12, and 15 inches). The overall effectiveness of the lowered jump-outs in allowing white-tailed deer to jump down, regardless of the height and setback of the bar, was only just above 5% (no improvement). For mule deer the effectiveness of the lowered jump-outs in allowing them to jump down, regardless of the height and setback of the bar, was about 64% (this was double the effectiveness of non-modified jump-outs).
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".