Diversity and life-cycle analysis of Pacific Ocean zooplankton by videomicroscopy and DNA barcoding: Crustacea
Bibliographic record
Abstract
Crustacea larvae and adults make up a large fraction of the biomass and number of organisms in both holoplankton (organisms that spend their entire lives in the plankton) and meroplankton (organisms that spend their larval stages in the plankton). The life cycles of these animals can be studied by raising individuals and studying them longitudinally in the laboratory, but this method can be very laborious. Here we show that DNA sequencing of a small element in the mitochondrial DNA (DNA barcoding) makes it possible to easily link life-cycle phases 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, and individual crustacean specimens were documented by videomicroscopy. 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, 609 COI sequences were obtained falling into 169 Barcode Identification Numbers (BINs), of which 85 correspond to recognized species. 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".