Research Article Establishing a baseline for early detection of non-indigenous species in ports of the Canadian Arctic
Bibliographic record
Abstract
The combination of global warming, resource exploitation and the resulting increase in Arctic shipping activity are expected to increase the risk of exotic species introductions to Arctic waters in the near future. Here, we provide for the first time a benthic invertebrate survey for non-indigenous species (NIS) from the Canadian Arctic coasts, incorporating historical information to identify new records. The top three ports at highest risk for introduction of NIS of the Canadian Arctic were surveyed: Churchill (Manitoba), Deception Bay (Quebec) and Iqaluit (Nunavut). A total of 236 genera and species were identified. Based on cross referencing comparisons of contemporary and historical information on species composition and distributions, 14.4 % of the taxa identified can be considered new records within the port regions surveyed and 7.2 % within the more extended, adjacent surrounding regions. Increased survey effort is the most likely explanation for the majority of new occurrences, however, a small number of records (n=7) were new mentions for Canada and were categorized as cryptogenic since we could not confidently describe them as being either native or introduced. Further research is required to better understand the status of these new taxa. This study provides a benchmark for early detection for benthic invertebrates in the region. Significant costs and intensive labor are involved in monitoring and in early detection surveys, but
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".