Importance of non-journal literature in providing evidence for predator conservation
Bibliographic record
Abstract
The literature other than scientific journals (non-journals) is a valuable, but scattered and rarely used, source of evidence of the effectiveness of interventions applied for protection from mammalian predators. This study describes how journals and non-journals differ in relation to study designs, types of interventions, predator species, countries, and publication bias. I collected 411 journal cases (226 publications) and 97 non-journal cases (64 publications) covering the period 1955–2020, five study designs, six interventions, 28 species and 50 countries. Non-journals were important for two predators (leopard Panthera pardus and snow leopard P. uncia) and four countries (Canada, India, Russia and Sri Lanka). These species and countries have been affected by human-predator conflicts and the use of non-journals should become a habitual practice to mitigate conflicts. Information on other species and countries, and all study designs and interventions, was provided mostly or only in peer-reviewed journals. This study helps make the use of non-journals easier for researchers and conservation practitioners by providing and explaining a list of relevant literature and online resources.
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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 | 0.281 | 0.705 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.068 | 0.062 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".