Distribution of Different Scrippsiella acuminata (Dinophyta) Cyst Morphotypes in Surface Sediments of the Black Sea: A Basin Scale Approach
Bibliographic record
Abstract
Plankton cyst abundance and distribution is controlled by multiple factors. The stress linked to the fluctuations and variations of the environmental conditions in the water column is a major vector of encystment and intraspecific variability is an important adaptive strategy. The present study aims to disclose a link between the spatial distribution and abundance of different cyst morphotypes ofScrippsiella acuminatacomplex in surface sediments collected in the Black Sea at 34 sites and selected environmental variables. With this purpose, a basin scale data set was analyzed for patterns of intraspecific spatial heterogeneity. Redundancy analysis (RDA) was implemented to identify explanatory environmental variables associated with the cyst morphotypes abundance. Environmental multiyear data were used to ensure better approximation of a model that links environmental gradients with cyst abundance. Our results show that allS. acuminatacysts morphotypes are significantly correlated to one or a combination of the environmental variables, i.e., salinity, temperature and nutrients (nitrates and phosphates). The geographical distribution ofScrippsiellablooms in the Black Sea indicates that the interplay between the planktonic and benthic habitat of the dinoflagellate gives toS. acuminatathe advantage to dominate in the plankton communities.
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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) | 0.000 | 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".