A screening procedure for potential tunicate invaders of Atlantic Canada
Bibliographic record
Abstract
The conditions for a successful invasion involve the intersection of a species, its vector, and an appropriate receiving environment. Species distribution (biogeography) and availability of a shipping vector were used as filters to reduce a list of 57 tunicates with a history of invasion in marine or estuarine waters worldwide, to a more manageable basis for a “watch list” or “trigger list” for non-indigenous invasive tunicates in Atlantic Canada. Seven species from the worldwide invasives list were already present in Atlantic Canada: the non-indigenous Styela clava, Ciona intestinalis (cryptogenic in southern Nova Scotia but non-indigenous in northern Atlantic Canada), Botryllus schlosseri, Botrylloides violaceus and Molgula manhattensis, and the native Aplidium glabrum and Didemnum candidum. Nine species, not currently present in Atlantic Canada, were removed from the analysis due to insufficient distribution data. All of the remaining 41 species co-occurred in one or more bioregions with species presently found in Atlantic Canada. Examination of distributions relative to shipping patterns eliminated eight species not present in the areas with the most shipping traffic to Atlantic Canada: the eastern seaboard of the USA, the Caribbean Sea, northern Europe and the Mediterranean Sea. A climate zone filter to remove species found only in subtropical or tropical waters eliminated 21 species. Applying both the shipping and climate zone filters resulted in a “watch list” of 17 tunicate species considered the most likely to successfully invade Atlantic Canada: Ascidia sydneiensis, Ascidiella aspersa, Botrylloides leachi, Clavelina lepadiformis, Cnemidocarpa irene, Corella eumyota, Cystodytes dellechiajei, Didemnum vexillum, Diplosoma listerianum, Perophora japonica, Perophora multiclathrata, Phallusia mammillata, Polyandrocarpa zorritensis, Polyclinum constellatum, Styela canopus, Styela plicata, and Symplegma brakenhielmi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".