Combining statolith element composition and Fourier shape data allows discrimination of spatial and temporal stock structure of arrow squid (<i>Nototodarus gouldi</i>)
Bibliographic record
Abstract
While arrow squid (Nototodarus gouldi) in Australia are currently managed as a single population, biological differences in individuals between locations of capture suggests these are separate stocks requiring stock-specific harvest strategies. We used two techniques to derive information about stock structure from different parts of the life cycle, providing a novel holistic approach to exploring stock structure. This study combined two techniques, statolith shape and statolith elemental composition, to determine dispersal patterns of N. gouldi between regions and evidence of separate stocks. While adult statolith shape provided evidence that adults caught in the two locations belonged to different stocks, statolith elemental composition suggested that N. gouldi caught at each location had hatched throughout their distribution, with egg mass and juvenile drift potentially facilitated by seasonal longitudinal ocean currents. However, there was evidence of asymmetry in ontogenetic movement of N. gouldi, with adults in Victoria contributing more to the Great Australian Bight stock than vice versa and with the implication that the Victorian stock may need to be managed as the source stock.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".