Grand Challenges in the Management and Conservation of North American Inland Fishes and Fisheries
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 Even with long-standing management and extensive science support, North American inland fish and fisheries still face many conservation and management challenges. We used a grand challenges approach to identify critical roadblocks that if removed would help solve important problems in the management and long-term conservation of North American inland fish and fisheries. We identified seven grand challenges within three themes (valuation, governance, and externalities) and 34 research needs and management actions. The major themes identified are to (1) raise awareness of diverse values associated with inland fish and fisheries, (2) govern inland fish and fisheries to satisfy multiple use and conservation objectives, and (3) ensure productive inland fisheries given nonfishing sector externalities. Addressing these grand challenges will help the broader community understand the diverse values of inland fish and fisheries, promote open forums for engagement of diverse stakeholders in fisheries management, and better integrate the inland fish sector into the greater water and land use policy process.
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.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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