Species richness and the level of knowledge of the bryozoan fauna of the Arctic region
Bibliographic record
Abstract
The publication presents the results of the analysis of retrospective data and samples of bryozoans collected in different seas and areas of the Arctic region during recent 30 years. To date, 518 species of bryozoans have been recorded in the Arctic, which is on 26.4% more than previously registered. The level of increase in species numbers in the species lists of the regional faunas was different in different areas. In the waters of Greenland, the found species diversity of bryozoans was on 12% higher; in the Barents and Kara Seas – on 18 and 19%, respectively; in the Laptev and East Siberian seas – on 30%; in the Faroe Islands waters on 30% than it was previously marked. In the Icelandic waters and the Chukchi Sea, the number of bryozoan species is richer by five and two times respectively than it was considered earlier. Our assessment of the modern knowledge of the fauna of this group using the method of rarefaction showed that the bryozoan fauna is still underexplored. The Chao metric calculations also indicate that expected species richness would increase by 10–30% in different areas of the Arctic in case of additional sampling efforts. At the same time, a measure of taxonomic distinguish of the fauna allows to conclude that the species composition of bryozoans has already been sufficiently studied in most of the considered areas of the Arctic zone except in the waters of the Canadian Arctic Archipelago.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".