Comparison of approaches for modelling submerged aquatic vegetation in the Toronto and Region Area of Concern
Bibliographic record
Abstract
In freshwater aquatic ecosystems, submerged aquatic vegetation (SAV) is critical habitat for may fish species and provides a variety of ecosystem services including nutrient filtration and substrate stabilization. Characterizing habitats and assessing their suitability for fish and other aquatic and terrestrial organisms is an important component of delisting efforts in the Toronto and Region Area of Concern (AOC). The primary objective of this study was to develop a spatial model for SAV within the AOC. A variety of modelling options were explored with a two stage random forest model identified as the most accurate approach; a two stage boosted regression tree model yielded comparable accuracy but was more complicated and processing intensive to implement. The final models for presence (modelled first) and SAV percent cover (applied only where the presence model predicted SAV to occur) incorporated directionally weighted wind fetch, water depth, and clarity (Secchi depth) with relatively high predictive accuracy (87.1% for presence). When applied across the AOC, SAV was primarily found to occur within the Central Waterfront, particularly adjacent to and among the Toronto Islands. Outside of this area, SAV was generally sparse and confined to areas that were protected from wind and wave action from Lake Ontario. Future habitat creation and remediation efforts should therefore focus on creating habitat conducive to SAV establishment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".