Investigating the presence of abandoned, lost, and discarded fishing gear (ALDFG) to protect golden cod in the Gilbert Bay MPA
Bibliographic record
Abstract
Abandoned, lost, and discarded fishing gear (ALDFG) is a global environmental, economic, and social issue. This thesis examines the magnitude of ALDFG in Gilbert Bay, Labrador to protect the most genetically distinct population of Atlantic cod (Gadus morhua) in the Western Atlantic, whose numbers have declined since creation of the Marine Protected Area (MPA) in 2005. Expanding on community-led initiatives, key knowledge holders (n = 14) were interviewed to obtain qualitative and geospatial data to guide the understanding and investigation of ALDFG. According to knowledge holders, ALDFG was not believed to be an issue impacting the Gilbert Bay cod, primarily due to commercial scallop fishers incidentally dragging up lost gear. During retrieval, a total of 66 sea-based sites were investigated, yielding a single cod trap. Knowledge holders also noted land-based gear in derelict stages and on wharves as an area of concern. As a result, 18 land-based sites were identified, retrieving various amounts of fishing nets, trawl lines, crab pots, cod traps, and fishing rope from 10 different sites between July and November of 2021. Data collected from the interviews, literature review, and field work are also used to provide further policy recommendations to ALDFG and fisheries management for Gilbert Bay and the region overall.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".