Elemental fingerprints of southern calamary (<i>Sepioteuthis australis</i>) reveal local recruitment sources and allow assessment of the importance of closed areas
Bibliographic record
Abstract
Movement of individuals over a range of temporal and spatial scales is a critical process in determining the structure and size of populations. For most marine species, a substantial amount of movement that is responsible for connecting subpopulations occurs when individuals are too small and numerous to be tagged using conventional methods. Using the elemental fingerprints of the statoliths of the squid Sepioteuthis australis and a robust machine learning classification technique, this study determined that newly hatched squid had elemental signatures that exhibited sufficient spatial variation to act as natural tags for natal origin and that elemental signatures can be used to allocate adult squid back to their natal site. Between 55% and 84% of the adult squid caught throughout the east and southeast of Tasmania, Australia, were classified back to an area that is closed to commercial fishing over much of the peak spawning period, and this was the only location with substantive evidence of natal recruitment. Although many studies have demonstrated the potential of this approach to discern connectivity between population units, few studies have successfully done so by then examining the trace element profiles of adults in addition to those of hatchlings as we have demonstrated with S. australis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".