Rich diversity and active spatial–temporal dynamics of<i>Thalassiosira</i>species revealed by time-series metabarcoding analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Thalassiosira is a species-rich genus in Bacillariophyta that not only contributes positively as primary producer, but also poses negative impacts on ecosystems by causing harmful algal blooms. Although taxonomical studies have identified a large number of Thalassiosira species, however, the composition of Thalassiosira species and their geographical distribution in marine ecosystems were not well understood due primarily to the lack of resolution of morphology-based approaches used previously in ecological expeditions. In this study, we systematically analyzed the composition and spatial–temporal dynamic distributions of Thalassiosira in the model marine ecosystem Jiaozhou Bay by applying metabarcoding analysis. Through analyzing samples collected monthly from 12 sampling sites, 14 Thalassiosira species were identified, including five species that were not previously reported in Jiaozhou Bay, demonstrating the resolution and effectiveness of metabarcoding analysis in ecological research. Many Thalassiosira species showed prominent temporal preferences in Jiaozhou Bay, with some displaying spring–winter preference represented by Thalassiosira tenera, while others displaying summer–autumn preference represented by Thalassiosira lundiana and Thalassiosira minuscula, indicating that the temperature is an important driving factor in the temporal dynamics. The application of metabarcoding analysis, equipped with appropriate molecular markers with high resolution and high specificity and databases of reference molecular marker sequences for potential all Thalassiosira species, will revolutionize ecological research of Thalassiosira species in Jiaozhou Bay and other marine ecosystems.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 it