Bibliographic record
Abstract
Huge flights of Canada geese turn off local park visitors with their messy, smelly "business cards." The superabundant white-tailed deer we love to watch also can do a number on your car at night and host the ticks that carry Lyme Disease. Blackbirds and gulls and coyotes and other critters bring their own problems when their numbers get out of hand. Most such problems reach their highest profile in urban/suburban areas where traditional animal-control techniques such as hunting and trapping are frowned upon or illegal. More and more people are calling for wildlife managers to use "fertility control"–-but is that concept really feasible on populations of free-ranging wildlife? The definitive answers–in the form of the latest science–are contained in a new Technical Review titled Wildlife Fertility Control. The 29-page Review notes that in the past, fertility control has been far less successful than observers had hoped, but thanks to new findings about animal reproductive systems, the technology is advancing rapidly and is being tested on several species on a small scale. Hurdles include the need to develop and commercialize effective vaccines or baits, cost-effective delivery systems, and public-agency acceptance of the technique. The new publication states that "birth control" will undoubtedly play a role in the science of wildlife management in the future. Managers face two major challenges: integrating contraceptive tactics with more conventional ways of managing critter numbers, and giving the public accurate information about the feasibility of using fertility control vs. lethal methods to reduce populations of deer and other long-lived species.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.019 |
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".