Integrated restoration prioritization – A multi-discipline approach in the Greater Toronto Area
Bibliographic record
Abstract
Ecosystem restoration planning requires an integrated approach considering many components of the natural system when prioritizing where and what to restore. Toronto and Region Conservation Authority and partners have developed a multi-discipline and multi-benefit approach to restoration planning that facilitates effective restoration works, which contribute to realizing regional watershed objectives pertaining to natural system functions. Through various long term monitoring and modeling initiatives, Toronto and Region Conservation Authority has amassed a wealth of knowledge on terrestrial biodiversity, aquatic ecosystems, hydrology, and headwater conditions. The aim of Integrated Restoration Prioritization is to identify impairments and threats to ecosystem function as a means to improve the delivery of ecological goods and services. Consolidating data and comparing discrete areas based on different parameters and thresholds can help direct decision making for future restoration initiatives. The first iteration of the Integrated Restoration Prioritization analyzed existing datasets, identified gaps, and made recommendations for future monitoring. This approach will assist with delisting Beneficial Use Impairments #14 Loss of Fish and Wildlife Habitat and #3 Degradation of Fish and Wildlife Populations within the Toronto Remedial Action Plan area. Further, the Integrated Restoration Prioritization will assist in implementing the recommendations made in watershed planning documents pertaining to fisheries and natural heritage management. Specifically, the Integrated Restoration Prioritization will identify where impairments to ecological function are located, ensure habitats and corridor linkages are protected or restored, and prioritize local and upstream catchments that could contribute most to improving the natural system if restored.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".