Differential ecological adaptation of diverse <i>Chaetoceros</i> species revealed by metabarcoding analysis
Bibliographic record
Abstract
Abstract Chaetoceros is a species‐rich genus of marine planktonic diatoms that play an important role in marine ecosystems both as primary producers and as species causing harmful algal blooms (HABs). In this study, we analyzed the composition and dynamic changes in Chaetoceros species in Jiaozhou Bay, China, using metabarcoding analysis of time‐series samples for the first time. Through analyzing samples collected monthly from 12 sampling sites, 25 species of Chaetoceros were detected, including nine species that were not previously reported in this ocean region, highlighting the strength of metabarcoding analysis of Chaetoceros species. Many Chaetoceros species showed strong seasonal preferences, with some species dominating in winter and spring, while others showing prominence in summer and autumn, suggesting sophisticated differential ecological adaptation. With the construction of more comprehensive datasets of reference barcodes, metabarcoding analysis could be used as the next‐generation method in ecological research on Chaetoceros species and phytoplankton species of other genera.
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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 | 0.001 |
| 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".