Navigating Nunatsiavut’s Arctic Charr: A Simultaneous Commercial and Subsistence Fishery with Many Unknowns
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
We explore optimal harvest conditions for Nunatsiavut’s Arctic charr, a data-deficient yet economically and culturally important fishery for Labrador Inuit. In the past, arbitrarily set quotas in the absence of data on science and climate shifts have led to sustainability concerns. The fishery, adhering to conservation principles, continues at low intensity today, so as to support local employment and maintain sociocultural values, despite its low economic viability. Using the only available data for Nain’s commercial fishery, we estimate intrinsic growth, catchability, and carrying capacity, which we then use in a bioeconomic model to estimate maximum economic yield. Results indicate that foregone commercial harvests are 75%–93% below optimal, before accounting for subsistence harvest. Improved understanding of the conditions under which subsistence and commercial fishing coexist alongside more investments in data collection to address scientific uncertainty can help provide clearer management guidance to meet harvest needs of both sectors and allow for better policies and governance of the fishery.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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