Integrating Expert Perceptions into Food Web Conservation and Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Decision‐makers often rely on expert knowledge, especially in complex and data‐poor social‐ecological systems (SESs). However, expert knowledge and perceptions of SES structure and function vary; therefore, understanding how these perceptions differ is critical to building knowledge and developing sustainability solutions. Here, we quantify how scientific, local, and traditional knowledge experts vary in their perceptions of food webs centered on Pacific herring—a valuable ecological, economic, and cultural resource in Haida Gwaii, BC, Canada. Expert perceptions of the herring food web varied markedly in structure, and a simulated herring recovery with each of these unique mental models demonstrated wide variability in the perceived importance of herring to the surrounding food web. Using this general approach to determine the logical consequences of expert perceptions of SES structure in the context of potential future management actions, decision‐makers can work explicitly toward filling knowledge gaps while embracing a diversity of perspectives.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it