Sources and Rates of Errors in Methods of Individual Identification for North Atlantic Right Whales
Bibliographic record
Abstract
Many long-term studies of wildlife populations rely on individual identification based on natural markings or genetic profiling, or both. However, only rarely are these 2 independent data sets systematically compared with each other to estimate the error rates inherent in these studies. Here, >25 years of photo-identification data on the endangered North Atlantic right whale (Eubalaena glacialis) were compared with high-resolution genetic profiles, available for >75% of the individuals in the photo-identification catalog, in order to identify sources and rates of errors associated with both methods of individual identification. The resulting estimates were 0.0308 errors/identification for the photo-identification data, and 0.00121 errors/locus and 0.0327 errors/multilocus profile for the genetic data. These are among the lowest error rates yet reported, and indicate that the approaches used provide reliable means of individual identification for this species. However, despite these low error rates, the large size of the data sets results in a nonnegligible estimated number of errors, indicating that the potential for these errors needs to be incorporated into other analyses that are based on these data. A similar situation likely exists in other long-term studies where, although error rates are assumed to be low, the size of the data set results in a large number of errors that will influence subsequent analyses. Regularly conducting and reporting extensive database comparisons such as this is invaluable for maintaining the integrity of long-term data sets by identifying where sources of error are occurring and how protocols can be improved to lower error rates in the future.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".