Biodiversity and Spatial-Temporal Dynamics of Margalefidinium Species in Jiaozhou Bay, China
Bibliographic record
Abstract
Many Margalefidinium species are cosmopolitan harmful algal bloom (HAB) species that have caused huge economic and ecological damage. Despite extensive research on Margalefidinium species, the biodiversity and spatial-temporal dynamics of these species remain obscure. Jiaozhou Bay is an ideal area for HAB research, being one of the earliest marine survey areas in China. In this study, we carried out the first metabarcoding study on the temporal and spatial dynamics of Margalefidinium species using the 18S rDNA V4 region as the molecular marker and samples collected monthly at 12 sampling sites in Jiaozhou Bay in 2019. Two harmful Margalefidinium species (M. polykrikoides and M. fulvescens) were identified with potentially high genetic diversity (although we cannot rule out the possibility of intra-genome sequence variations). Both M. polykrikoides and M. fulvescens demonstrated strong temporal preference with a sharp peak of abundance in early autumn (September), but without showing strong location preference in Jiaozhou Bay. Our results revealed that temperature might be the main driver for their temporal dynamics. Knowledge of biodiversity and spatial-temporal dynamics of the Margalefidinium species may shed light on the understanding of mechanisms underlying strongly biased occurrences of Margalefidinium blooms recorded globally.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".