Bibliographic record
Abstract
species fe a tu re Ontario, covering an area of 2.8 million km2 (1.1 million miles2), is one of the largest jurisdictions in North America (Figure 1). There are in excess of 250,000 inland lakes, thousands of kilometres of streams and rivers, and waters of 4 of the Laurentian Great Lakes within the province of Ontario. It has been estimated that Ontario accounts for approxi-mately 15 % of the world’s freshwater. Ontario waters are known to support 165 species of fish, 128 of which are native species (Mandrak and Crossman 1992). One of the most pressing ecological issues today involves the transfer and spread of non-indigenous species. Non-indigenous species may be defined as plants or animals which are transferred to areas out-side of their historic or natural geographic range (Fuller et al. 1999). Invasive aquatic species can have profound eco-nomic and ecological impacts. An estimated $500 million is spent annually by Canada on efforts to con-trol invasive aquatic species in the Great Lakes (Commissioner of the Environment and Sustainable Development 2001). MacIsaac (2003) estimated costs of up to $750 million annually for damage to aquatic ecosystems in Canada. Worldwide, the impact of invasive aquatic organisms is estimated to cost more than $314 billion per year in damage and control costs (Pimentel 2002). From an ecological perspective, invasive species can often cause major disruptions to native fauna. Native species may be reduced in numbers, driven to extinction directly by competition and predation, or be genetically altered by hybridization with non-indigenous species. Invasive aquatic species are considered to be one of the major threats to fish species at risk in the Great Lakes area (A. Dextrase,
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.020 |
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".