Dinoflagellate cyst assemblages as tracers of sea‐surface conditions in the northern North Atlantic, Arctic and sub‐Arctic seas: the new ‘<i>n</i> = 677’ data base and its application for quantitative palaeoceanographic reconstruction
Bibliographic record
Abstract
Abstract The distribution of dinoflagellate cyst (dinocyst) assemblages in surface sediment samples from 677 sites of the northern North Atlantic, Arctic and sub‐Arctic seas is discussed with emphasis on the relationships with sea‐surface parameters, including sea‐ice cover, salinity and temperature of the coldest and warmest months. Difficulties in developing a circum‐Arctic data base include the morphological variation within taxa (e.g. Operculodinium centrocarpum , Islandinium ? cezare and Polykrikos sp.), which probably relate to phenotypic adaptations to cold and/or low salinity environments. Sparse hydrographical data, together with large interannual variations of temperature and salinity in surface waters of Arctic seas constitute additional limitations. Nevertheless, the use of the best‐analogue technique with this new dinocyst data base including 677 samples permits quantitative reconstruction of sea‐surface conditions at the scale of the northern North Atlantic and the Arctic domain. The error of prediction calculated from modern assemblages is ±1.3 °C and ±1.8 °C for the temperature of February and August, respectively, ±1.8 for the salinity, and ±1.5 months yr −1 for the sea‐ice cover. Application to late Quaternary sequences from the western and eastern subpolar North Atlantic (Labrador Sea and Barents Sea) provide reconstructions compatible with those obtained using the previous dinocyst data base ( n = 371), which mainly included modern data from the northern North Atlantic. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".