Exploring the use of Fourier transform near infrared spectroscopy for aging Newfoundland and Labrador Atlantic cod (Gadus morhua)
Bibliographic record
Abstract
Fourier transform near-infrared spectroscopy (FT-NIRS) is a new tool for aging fish otoliths which has been shown to be potentially faster than counting otolith annuli (traditional aging) while having similar accuracy and precision. We investigated the use of FT-NIRS for aging Atlantic cod ( Gadus morhua ) otoliths from North Atlantic Fisheries Organization (NAFO) Divisions 2J, 3K, 3L, 3N, 3O, and 3Ps. Two types of calibration models (partial least squares (PLS) regression and principal component analysis combined with multinomial regression) were fit between traditional age estimates for otoliths and either their full FT-NIR spectra or an informative region of their spectra. Model fits were compared across calibration model types, portion of spectra used, and between models fit to individual versus all NAFO Divisions combined. A time-cost analysis comparing traditional aging versus combinations of FT-NIRS and traditional aging was performed. Good model fits (root mean square error ≤ 1; adjusted-R 2 ≥ 0.85) were generally achieved for at least one calibration model fit across all NAFO Divisions and for individual Divisions. PLS and multinomial calibration model performances were very similar for models fit across all NAFO Divisions, but varied due to small sample sizes for individual Divisions. Portion of the FT-NIR spectra used to fit models had the least impact. Time-cost analysis suggested that labour savings of 23 % (6 calibration models) to 40 % (1 calibration model) could be obtained. Our results show the FT-NIRS provides reliable age estimates for Atlantic cod with similar accuracy and precision to traditional aging while also providing labour cost savings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".