Predicting adaptations of fish and fishing communities to rapid climate velocities in Canadian waters: A systematic review
Bibliographic record
Abstract
Communities along Canada's three coastlines heavily rely on the $3,500 million marine aquaculture and fishery sectors, but the changing climate threatens their viability, making adaptation to climate change in fisheries increasingly critical in the international setup. As ocean conditions are expected to undergo dramatic shifts, including significant changes in sea surface temperatures, ocean acidification levels, ocean currents, weather patterns, and overall ecosystem dynamics, proactive measures are essential to address the impacts on fish populations, marine resource sustainability, and the livelihoods of coastal communities. This study aims to investigate the relationship between fishing and climate velocity, specifically focusing on its effects on marine fish abundance and distribution. Simultaneously, it seeks to understand how fishers and fishing communities adapt to variations in species ranges while identifying incentives that promote the sustainability of marine life and economic stability in the context of climate change. To address these questions, we conducted a systematic review of existing research published between 2012 and 2022, spanning multiple databases, including the ACM Digital Library, PubMed, Google Scholar, Scopus, IEEE Xplore, and Web of Science. Out of a total of 4956 studies identified, 23 studies meeting predetermined inclusion criteria were selected. Our analysis uncovered a strong correlation between climate change and habitat modification, significantly impacting multiple fish species. Furthermore, our findings revealed that these risks have direct consequences on fish distribution, as well as on the angling industry and the communities that rely on it. By examining this extensive literature, we aimed to gain valuable insights, identify trends, and uncover any knowledge gaps related to fish distribution and abundance in Canada's waters, with the hope of informing policies and strategies that can help safeguard the future of these vital industries and the communities they support in the face of ongoing climate change.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".