Biogeography of dinoflagellate cysts in northwest Atlantic estuaries
Bibliographic record
Abstract
Few biogeographic studies of dinoflagellate cysts include the near-shore estuarine environment. We determine the effect of estuary type, biogeography, and water quality on the spatial distribution of organic-walled dinoflagellate cysts from the Northeast USA (Maine to Delaware) and Canada (Prince Edward Island). A total of 69 surface sediment samples were collected from 27 estuaries, from sites with surface salinities >20. Dinoflagellate cysts were examined microscopically and compared to environmental parameters using multivariate ordination techniques. The spatial distribution of cyst taxa reflects biogeographic provinces established by other marine organisms, with Cape Cod separating the northern Acadian Province from the southern Virginian Province. Species such as Lingulodinium machaerophorum and Polysphaeridinium zoharyi were found almost exclusively in the Virginian Province, while others such as Dubridinium spp. and Islandinium? cezare were more abundant in the Acadian Province. Tidal range, sea surface temperature (SST), and sea surface salinity (SSS) are statistically significant parameters influencing cyst assemblages. Samples from the same type of estuary cluster together in canonical correspondence analysis when the estuaries are within the same biogeographic province. The large geographic extent of this study, encompassing four main estuary types (riverine, lagoon, coastal embayment, and fjord), allowed us to determine that the type of estuary has an important influence on cyst assemblages. Due to greater seasonal variations in SSTs and SSSs in estuaries compared to the open ocean, cyst assemblages show distinct latitudinal trends. The estuarine context is important for understanding present-day species distribution, the factors controlling them, and to better predict how they may change in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".