Evaluation of Five Freshwater Fish Screening-Level Risk Assessment Protocols and Application to Non-Indigenous Organisms in Trade in Canada
Bibliographic record
Abstract
iv RESUME v INTRODUCTION 1 Background 2 Purpose 3 METHODS 3 Selection and Evaluation of SLRA Protocols 3 Freshwater Fishes in Live Trade in Canada 5 Fish Species in Trade List 5 Habitat Matching 6 Climate Matching 7 Screening Species Using SLRA Protocols 8 RESULTS AND DISCUSSION 8 Evaluation of SLRA Protocols 8 Fish Species in Trade List, Habitatand Climate-Match Analysis 12 Screening Species Using SLRA Protocols 12 CONCLUSIONS 14 RECOMMENDATIONS 14 REFERENCES CITED 15 APPENDIX 1 – FISK Risk Assessment Tool 17 APPENDIX 2 – Modified Alberta Risk Assessment Tool 22 APPENDIX 3 – Montreal Risk Assessment Tool 40 APPENDIX 4 – GLANSIS Risk Assessment Tool 46 APPENDIX 5 – Notre Dame Statistical Risk Assessment Tool 60 APPENDIX 6 – Master List of Fishes in Trade in Canada 62
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".