Thirty-two essential questions for understanding the social–ecological system of forage fish: the case of pacific herring
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 Forage fishes are ecologically and economically important low trophic level species, and in recent years interest in their biology and management has intensified. Pacific Herring are emblematic of the management issues facing forage species—they are central components of the Northeast Pacific pelagic food web and support important commercial fisheries. In addition, the importance of Herring to indigenous peoples have made them cultural keystone species. We employed a participatory process to promote collaborative priority-setting for this critical forage species. Working with managers, the fisheries industry, indigenous peoples, and scientists, we co-constructed a conceptual model of the Pacific Herring social–ecological system () in the Northeast Pacific. We then identified a set of questions, that, if answered, would significantly increase our ability to sustainably manage the Herring . Our objective was to generate a road map for scientists who wish to conduct useful forage fish research, for resource managers who wish to develop new research efforts that could fill critical gaps, and for public agencies and private foundations seeking to prioritize funding on forage fish issues in the Pacific. With this socio-cultural centrality comes complexity for fisheries management. Our participatory process highlighted the value of conceptualizing the full SES, overcame disciplinary differences in scientific approaches, research philosophy, and language, and charted a path forward for future research and management for forage species.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 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) | 0.000 | 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