Richness and zoogeography of ascidians (Tunicata: Ascidiacea) in eastern Canada
Bibliographic record
Abstract
Managers and policymakers in eastern Canada embrace science-based management of nonindigenous species and may benefit from having comprehensive regional species checklists at subnational jurisdictional levels. In this paper, regional checklists provide an account of the richness of ascidians in eastern Canada. Records of 58 ascidians resulted from reviewing 108 published sources, accessing data from two online databases, and collecting some common indigenous ascidian specimens. Analysis comparing the similarity of species among nine regions indicates that there is greater similarity in species composition between contiguous regions than between noncontiguous regions and suggests that there are four zoogeographic clusters in eastern Canada. Our checklists can inform managers and policymakers of the diversity of the ascidian taxa and can minimize taxonomic uncertainties of established nonindigenous and prospective invading species, for example, by identifying indigenous species that are congeners of nonindigenous species. The maintenance of checklists can be a valuable tool for the management of nonindigenous species as baselines to estimate changes in richness and to document the invasion status of nonindigenous species over time. For example, more importance can be placed on the spread of nonindigenous ascidians from one zoogeographic cluster to another than spread within the same cluster.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".