Diversity and life-cycle analysis of Pacific Ocean zooplankton by video microscopy and DNA barcoding: Crustacea
Bibliographic record
Abstract
Determining the DNA sequencing of a small element in the mitochondrial DNA (DNA barcoding) makes it possible to easily identify individuals of different larval stages of marine crustaceans without the need for laboratory rearing. It can also be used to construct taxonomic trees, although it is not yet clear to what extent this barcode-based taxonomy reflects more traditional morphological or molecular taxonomy. Collections of zooplankton were made using conventional plankton nets in Newport Bay and the Pacific Ocean near Newport Beach, California (Lat. 33.628342, Long. -117.927933) between May 2013 and January 2020, and individual crustacean specimens were documented by video microscopy. Adult crustaceans were collected from solid substrates in the same areas. Specimens were preserved in ethanol and sent to the Canadian Centre for DNA Barcoding at the University of Guelph, Ontario, Canada for sequencing of the COI DNA barcode. From 1042 specimens, 544 COI sequences were obtained falling into 199 Barcode Identification Numbers (BINs), of which 76 correspond to recognized species. For 15 species of decapods (Loxorhynchus grandis, Pelia tumida, Pugettia dalli, Metacarcinus anthonyi, Metacarcinus gracilis, Pachygrapsus crassipes, Pleuroncodes planipes, Lophopanopeus sp., Pinnixa franciscana, Pinnixa tubicola, Pagurus longicarpus, Petrolisthes cabrilloi, Portunus xantusii, Hemigrapsus oregonensis, Heptacarpus brevirostris), DNA barcoding allowed the matching of different life-cycle stages (zoea, megalops, adult). The results show the utility of DNA barcoding for matching life-cycle stages as well as for documenting the diversity of this group of organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".