A general model of detectability using species traits (In Press)
Bibliographic record
Abstract
Biological surveys underpin most ecological studies. They may be used to determine the distribution and abundance of species, monitor changes in populations or communities and as part of ecological impact assessments. However, numerous studies have demonstrated that detections of plant and animal species are imperfect, so species can remain undetected during a biological survey despite being present (McArdle 1990; Kery 2002; Kery & Gregg 2003; Slade, Alexander & Kettle 2003; Tyre et al. 2003; Bailey, Simons & Pollock 2004; de Solla et al. 2005; Wintle et al. 2005; MacKenzie et al. 2006; Alexander et al. 2009). Failure to account for imperfect detectability in biological surveys may bias estimates of abundance or species richness, impair detection of change or identification of differences due to management actions, misinform management decisions and increase the risk of extinction of rare and endangered species (Wintle et al. 2012). Imperfect detection should be considered when designing surveillance programs (Regan et al. 2006; Hauser & McCarthy 2009), and early detection is critical for the successful management of invasive species (Timmins & Braithwaite 2002).
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".