Bibliographic record
Abstract
Abstract Aim Risk assessments in applied scientific disciplines have evolved somewhat in isolation, adopting conventions, assumptions and tools from other disciplines almost haphazardly. This editorial provides background for the articles in this special issue, which sample six broad themes in risk assessment in conservation biology and presenting new innovations and applications. Location Global. Methods The articles in the special issue address themes related to species distribution modelling, population viability analysis, threatened species management, biosecurity, uncertainty analysis, cost–benefit analysis and foresight. We sought articles that address new and emerging topics in each of these areas. Results The articles identify new and potentially useful innovations in a variety of areas relevant to conservation biology. Collectively, they paint a picture of risk assessment as an important element in supporting transparent, rational decisions and effective policy. Main conclusions Policy makers and conservation managers aspire to set evidence‐based priorities, and technical specialists aim to have their methods used in decision‐making. Scientists will succeed if, as the articles in this issue exemplify, they develop a sound understanding of the context of the decisions in which their tools are to be used and shape them accordingly.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".