Characterised and Projected Costs of Nonindigenous Species in Canada
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Biological invasions by nonindigenous species (NIS) can have adverse effects on economically important goods and services, and sometimes result in an 'invisible tax' on natural resources (e.g. reduced yield). The combined economic costs of NIS may be significant, with implications for environmental policy and resource management; yet economic impact assessments are rare at a national scale. Impacts of nuisance NIS may be direct (e.g. loss of hardwood trees) or indirect (e.g. alteration of ecosystem services provided by growing hardwoods). Moreover, costs associated with these effects may be accrued to resources and services with clear 'market' values (e.g. crop production) and to those with more ambiguous, 'non-market' values (e.g. aesthetic value of intact forest). We characterised and projected economic costs associated with nuisance NIS in Canada, through a combination of case-studies and an empirical model derived from 21 identified effects of 16 NIS. Despite a severe dearth of available data, characterised costs associated with ten NIS in Canadian fisheries, agriculture and forestry totalled $187 million Canadian (CDN) per year. These costs were dwarfed by the 'invisible tax' projected for sixteen nuisance NIS found in Canada, which was estimated at between $13.3 and $34.5 billion CDN per year. Canada remains highly vulnerable to new nuisance NIS, but available manpower and financial resources appear insufficient to deal with this problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".