Abundance of minke whales (<i>Balaenoptera acutorostrata</i>) in the Northeast Atlantic: variability in time and space
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
Regional sighting surveys with two independent observers on each vessel were conducted each year from 1996 to 2001. Northern minke whales (Balaenoptera acutorostrata) are mostly solitary animals and are only available for observation at moments when they surface to breath. Thus, a stochastic point process model is developed for how the data are generated. The hazard probability of initially sighting a whale that surfaces depends on relative spatial coordinates and on other covariates. The parameters of the model are estimated by maximum likelihood. To account for interannual variation in spatial distribution of minke whales, a random effects model is developed and estimated by comparing current and past (1989 and 1995) survey data. A simulation approach is taken to remove bias from parameter estimates and to assess the uncertainty in the results. For total abundance, the result is a log-normal confidence distribution with quantiles 107 205·exp(0.137z), i.e., an abundance estimate of 107 205 with a coefficient of variation of ≈0.14. Together with these and earlier survey data, past data on catch, markrecapture, and satellite tracking are reviewed to elucidate distribution and migration patterns in Northeastern Atlantic minke whales.
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 | 0.003 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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