Towards effective ecological restoration: Investigating knowledge co‐production on fish–habitat relationships with Aquatic Habitat Toronto
Bibliographic record
Abstract
Abstract For decades, the working paradigm for ecological restoration was independent operation of knowledge generators (researchers and scientists) and knowledge users (decision makers and practitioners), resulting in a knowledge–action gap. Knowledge co‐production is a collaborative process where research is conducted in a respectful and engaging manner with continuous knowledge exchange and heralded as a means of bridging the divide. Aquatic Habitat Toronto (AHT) is a unique consensus‐based partnership with diverse member agencies that engage in restoration ecology and practice along the Toronto Waterfront of Lake Ontario, Canada. Here, we examine the process that AHT uses to enable knowledge co‐production and identify associated benefits and challenges. Benefits to AHT's consensus‐based partnership include advanced notice of projects, access to diverse expertise and local knowledge, increased understanding of fish habitat, adoption of novel restoration techniques and more effective restoration and improved knowledge exchange, collectively mitigating the knowledge–action divide. Challenges of knowledge co‐production facilitated by AHT include consistent agency participation and meaningful engagement, closed or exclusive networks, time commitments and limited financial resources, evolving political landscapes, stability of funding cycles and issues stemming from varying goals and relevancy. Key recommendations for ensuring that knowledge co‐production results in actionable science and for maximizing the effectiveness of ecological restoration using AHT's format include securing long‐term and stable funding, developing relationships across agencies and allied partners, engaging early, outlining goals/objectives collaboratively, conducting before and after scientific monitoring, minimizing personal biases, periodically reviewing partnerships to maximize inclusivity, sharing successes (and failures) broadly, and providing open data. AHT embraces an approach that includes integrated planning with multi‐jurisdictional support with diverse partners at a tractable scale and we argue that this should be the standard model of aquatic ecosystem management.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".