Unsupervised Bayesian reconstruction of individual life histories from otolith signatures: case study of Sr:Ca transects of European eel (<i>Anguilla anguilla</i>) otoliths
Bibliographic record
Abstract
The reconstruction of individual life histories from chemical otolith measures is stated as an unsupervised signal-processing issue embedded in a Bayesian framework. This computational methodology was applied to a set of 192 European eel (Anguilla anguilla) otoliths. It provided a robust and unsupervised analysis of the individual chronologies of habitat use (either river, estuary, or coastal) from Sr:Ca measures acquired along an otolith growth axis. Links between Sr:Ca values and habitat, age, and season and the likelihood of the transitions from one habitat type to another were modelled. Major movement characteristics such as age at transition between habitats and time spent in each habitat were estimated. As a straightforward output, an unsupervised classification of habitat use patterns showed great variability. Using a hidden Markov model, 37 patterns of habitat use were found, with 20 different patterns accounting for 90% of the sample. In accordance with literature, residence behaviour was observed (28% of the eels). However, about 72% changed habitat once or several times, mainly before age 4. The potential application of this method to any other measures taken along an otolith growth axis to reconstruct individual chronologies gives a new insight in life history tactics analysis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".