Identifying pelagic fish eggs in the southeast Yucatan Peninsula using DNA barcodes
Bibliographic record
Abstract
In the waters surrounding Banco Chinchorro in the Mexican Caribbean are spawning and nursery areas for many types of fish. In this natural environment, as opposed to under controlled laboratory conditions, it is almost impossible to link an individual egg to the adult that laid it. This makes identifying the species of the eggs difficult. However, DNA barcodes have made this easier. In the present study, 300 eggs were processed for molecular analysis, from which 139 sequences were obtained. We identified 42 taxa (33 species with their binomial names), 35 genera, and 24 families. The identified eggs included those from Ariomma melanum, which is the first recording of this species in the Mexican Caribbean. Eggs from economically important fish species were also identified, including frigate tuna (Auxis thazard), crevalle jack (Caranx hippos), common dolphinfish (Coryphaena hippurus), sailfish (Istiophorus platypterus), white marlin (Kajikia albida), skipjack tuna (Katsuwonus pelamis), blackfin tuna (Thunnus atlanticus), and swordfish (Xiphias gladius). We have also described new morphological characteristics and captured photographs for 21 species, as well as obtained new information about spawning locality and time for 16 species. This valuable information will provide the basis to develop more effective conservation measures for sustainable fisheries and protection of the Mesoamerican Barrier Reef System.
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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".