Measuring 30 years of improvements to aquatic connectivity in the Greater Toronto Area
Bibliographic record
Abstract
Instream barriers (e.g. dams, weirs and road crossings) fragment aquatic habitat and prevent the upstream movement of fish, impairing the ability of fishes to complete critical life stages, access critical habitat and for dispersal among local populations. Mitigation efforts have improved aquatic connectivity to some degree, but it has been challenging to quantify the overall improvement in connectivity without long-term and costly field assessments. The development of spatially explicit habitat connectivity indices make it possible to evaluate current stream connectivity, and quantify the improvement prior mitigation projects have had on connectivity. We combined a list of instream barrier mitigation projects completed in five watersheds in the Toronto (Ontario, Canada) area from 1987–2016 (mitigated barriers) and a previously established inventory of all known instream barriers in 2016 (current barriers). The cumulative improvement to connectivity was measured for potadromous (remain in tributaries) and diadromous (move between tributaries and lake) fish species using the dendritic connectivity index. Aquatic connectivity improved for diadromous species between 0 and 14.5% and for potadromous species between 0.1 and 4.4% in the five studied watersheds. Some variation in improvement among the watersheds can likely be attributed to differences in mitigation strategies among the watersheds and a historical emphasis on mitigating instream barriers to benefit migratory salmonid species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".