CAISN: Abundance and biomass of benthic invertebrates collected in four ports of the Canadian Arctic during summers of 2011 and 2012
Bibliographic record
Abstract
The Canadian Aquatic Invasive Species Network (CAISN) was established with the goal of identifying and quantifying the vectors and pathways by which aquatic invasive species(AIS) enter Canada, determining factors that affect their colonization success, and developing risk assessment models for potential and existing AIS. This program ran between 2006-2011. The CAISN II project (2011-2016) was designed to provide a comprehensive profile of AIS in waters across Canada and develop and determine effectiveness of tools for early detection of, and rapid response to invaders. This resource contains the information on a CAISN II baseline study for benthic invertebrates in search of non-indigenous species (NIS) from the Canadian Arctic coasts made during summers 2011-2012. This survey identified as well native species by 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). It also includes information on Steesnby Inlet (Nunavut) that was sampled given that it is a proposed site for the construction of a new port. A total of 287 genera and species were identified. 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 dataset formed the basis of a PhD thesis (Goldsmit, Jesica 2016).
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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 | 0.005 |
| 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) | 0.002 | 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".