Balancing efficiency and rigour in horizon scanning: an application to the management of invasive non-indigenous species in the marine waters of Eastern Canada
Bibliographic record
Abstract
Horizon scanning, the systematic identification and risk assessment of potential non-indigenous species (NIS) for a given area of interest, is an important tool for preventative management of invasive NIS.However, existing horizon scans for invasive NIS exhibit methodological trade-offs, either being comprehensive in scope but resource-intensive (e.g.requiring large groups of experts) or resourceefficient but narrow in scope (e.g.focused on one taxon), neither of which are ideal for small-scale management contexts where managers are responsible for a broad scope of invasive NIS taxa but resources are limited.We developed a horizon scanning approach combining methodological efficiency with a broad scope and applied it to marine NIS in the Fisheries and Oceans Canada Maritimes Region (DFO Mar), a large geographic region in eastern Canada with a small management team that is a hotspot for marine NIS.We achieved a novel balance between comprehensiveness and efficiency by combining elements of existing horizon scanning methodologies with data-driven species filtering and an empirically optimized screening-level risk assessment (SLRA) protocol.We identified western Europe and eastern North America as the most likely source regions of marine NIS for DFO Mar, and compiled an initial list of 2525 potential NIS from these areas.After filtering this list for species of interest, using data from taxonomic and species distribution databases and the literature, we assessed 190 species using the SLRA protocol and flagged an additional 239 for future reassessment.Of the assessed species, 29 were determined to have a high relative risk of becoming invasive in DFO Mar, with 69 posing a moderate relative risk.The results of this horizon scan will inform marine invasive species management in DFO Mar.Additionally, the methodologies developed in this study can be adapted for use in horizon scanning exercises in other regions, particularly in small-scale management contexts.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".