Paralarval and juvenile cephalopods within warm-core eddies in the North Atlantic
Bibliographic record
Abstract
Many descriptions of paralarval and juvenile cephalopods are poor. By using DNA barcoding, a global bioidentification system for animals, along with morphological investigation, we can confirm species identifications. We have a better chance of eliminating misidentifications and, therefore, documenting the correct abundance and distribution of cephalopods within an area by combining morphological and molecular evidence. The central objectives of this study are to: (1) compare morphological vs molecular identification of cephalopods and (2) determine the occurrence of cephalopods within the deep scattering layer (DSL) within warm core eddies. The specimens reported here were collected between 2014 and 2016 during three transatlantic cruises from Galway, Ireland to St John's, Newfoundland, with a focus on assemblages in warm-core mesoscale eddies on the western part of the transect. Samples were collected from the DSL at multiple stations across mesoscale eddies. In total, 301 cephalopods belonging to 29 species were collected. Not only does our study increase the knowledge of abundance and diversity of pelagic cephalopods in this area, but it also provides sequences for species for which no comparative sequences were previously available. By examining the match/mismatch between morphological and molecular identifications, we highlight a need for revisions in some taxonomic groupings such as the family Cranchiidae.
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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 | 0.001 |
| 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".