Report of the workshop on age estimation in beluga: Beaufort, North Carolina, US 5-9 December 2011
Bibliographic record
Abstract
A workshop convened by C. Lockyer and A. A. Hohn to examine variation among readers in estimating beluga ages was held in Beaufort, North Carolina, US. Terms of Reference for the workshop included the following:1. Provide a guide as to acceptable levels of accuracy and precision for age reading that will enable ages to be used in population models.2. Conduct an inter-reader/laboratory comparison for calibration and standardization of age readings from GLG counts among all readers/laboratories.3. Provide information on validation that will enable GLG counts to be translated to real age.4. Produce a manual of guidelines for the preparation and reading of GLGs in beluga teeth.Presentations by participants are abstracted here. Then we report on the processes used to compare sections, images, and interpretation, and generate guidelines for best practices in beluga age estimation. A comparative study quantified differences among readers and found that precision of experienced readers was good, higher than reported for other odontocetes. Participants agreed that counting GLGs using well prepared thin sections was preferred because they are simpler to prepare than stained sections and there was more agreement among readers compared to using half sections. Examination of teeth from captive beluga as both untreated sections and stained sections and did not clarify the reading of wild beluga teeth. This Workshop concurred with Workshop 1 (Tampa 26-27 November 2011) that interpreting one GLG as an annual record is irrefutable. Guidelines for best practices were developed.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".