Fish ID face-off: A comparison of genetic barcoding and otolith shape analysis for streamlining species identification of mesopelagic fishes
Bibliographic record
Abstract
Identifying fish to species level is important for fisheries research as it ensures the accuracy of catch data reported by fishing vessels and informs best management strategies for harvested taxa. Genetic methods are one of the most common techniques used for identifying fish species but can be time-consuming and costly and may lead to incorrect or incomplete identifications if genetic baselines do not exist. Visual identifications using otolith shape are an inexpensive alternative that can be used to identify large numbers of fish samples quickly and with high accuracy. The objective of this study was to compare two methods of taxonomic identification ( i.e. , DNA barcoding of the COI-5P marker and visual identifications using body morphology, distinguishing features and otolith shape) for 240 specimens of mesopelagic fishes as they occupy a critical role in marine food webs. We also tested the effectiveness of geometric morphometrics in delineating species of mesopelagic fishes based on otolith shape. Our results showed that visual identifications agreed with genetic identifications 89 % of the time, and that both techniques were effective for identifying mesopelagic fishes. Additionally, we found that geometric morphometrics were successful in distinguishing mesopelagic fishes using otolith shape 86 % of the time. Otolith shape is a useful tool for the taxonomic identification of mesopelagic fishes in the Northeast Pacific Ocean and should be employed with higher frequency by fisheries researchers, especially in the absence of taxonomic or genetic expertise.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".