A general model of detectability using species traits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it