Age validation of Black Rockfish, Copper Rockfish, and Cabezon using secondary ion mass spectrometry (SIMS) to elucidate seasonal patterns in otolith stable oxygen isotopes
Bibliographic record
Abstract
Abstract Stock assessors commonly use models that incorporate biological data such as fish length and age to determine the status of fish stocks and how different management scenarios affect stock size. Ages used in assessment models need to be accurate and precise because ageing error can affect many model inputs and potentially result in stock mismanagement. Despite the requirement for sound age data, few studies have investigated accuracy of ages for groundfish captured in coastal nearshore waters off Oregon, USA. In this study, we validate otolith ages for Black Rockfish (Sebastes melanops), Copper Rockfish (Sebastes caurinus) and Cabezon (Scorpaenichthys marmoratus), three species with recreational and commercial importance to Oregon. Ages obtained by traditional break-and-burn methods were validated using secondary ion mass spectrometry (SIMS) to examine otolith stable oxygen isotope ratios (δ18O) over a fish’s lifetime. This technique relies on the inverse relationship that exists between otolith δ18O and ambient water temperature, and independent counts of δ18O maxima should be comparable to ages obtained by visual counts of otolith growth marks laid down during cold water periods. Locations of δ18O maxima in otolith chronologies matched well with locations of visual growth marks in otoliths of all three species, maxima counts were strongly positively correlated with age, and variation in otolith δ18O decreased with age. However, significant variability in the δ18O chronologies caused by variability in intra-seasonal upwelling and resulting water temperature variations made maxima counts difficult in several samples. Correct interpretation of chronologies required knowledge of location of the first annulus, the compression of growth zones with age, and an assumption of the seasonal amplitude of the ambient water δ18O.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".