A transformative approach to ageing fish otoliths using Fourier transform near infrared spectroscopy: a case study of eastern Bering Sea walleye pollock (<i>Gadus chalcogrammus</i>)
Bibliographic record
Abstract
We investigated the use of Fourier transform near infrared spectroscopy (FT-NIRS), which is a method of measuring light absorbance signatures, to derive ages from eastern Bering Sea walleye pollock (Gadus chalcogrammus) otoliths. This approach is based on a predictive model between near infrared spectra in the otolith and fish age, which is calibrated and validated. The advantage of FT-NIRS over traditional methods is the speed and repeatability with which age estimates are generated. The application of FT-NIRS to walleye pollock otoliths yielded r2 values between 0.91 and 0.95 for the calibration models and good validation performance (between 0.82 and 0.93). This approach can be expected to predict fish age within ±1.0 year of age 67% of the time. When comparing approaches, the FT-NIRS had as good or slightly better precision (75% agreement) than the traditional ageing (66% agreement) and showed little or no bias at age before 12 years of age. Once the predictive FT-NIR model is calibrated and validated, age estimates using FT-NIRS can be done at 10 times the rate compared to traditional methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".