Fish community indices of ecosystem health: How does Toronto Harbour compare to other Lake Ontario nearshore areas?
Bibliographic record
Abstract
We assessed fish community status for 16 nearshore areas in Lake Ontario and the upper St. Lawrence River, from 2006 to 2016, using complementary fish sampling gear types and protocols, boat electrofishing and trap nets, and the published aquatic ecosystem health indicators associated with them. Factors influencing Indices of Biotic Integrity scores included degree of exposure to the open-waters of Lake Ontario, effective fetch, and land cover and use in surrounding watersheds. Focusing on Toronto Harbour, we determined that Indices of Biotic Integrity scores were lower (45.1 and 45.6 for electrofishing and trap net gear types, respectively) than predicted (55.5 and 59.6) based on other Lake Ontario nearshore areas with similar physical/environmental conditions but seemed reasonable given the significant influence of Canada’s largest urban area, the City of Toronto. The proportion of fish community biomass comprised of piscivores (0.21 and 0.18 for electrofishing and trap nets, respectively) approached target levels (0.20) set for the Toronto Harbour fish community, and indicated a balanced trophic structure. On-going aquatic habitat remediation and creation projects on the Leslie Street Spit (including Tommy Thompson Park) and the Toronto Islands, should ensure maintenance or improvement in Indices of Biotic Integrity scores and aquatic ecosystem health generally.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".