Considering the ‘effort factor’ in fisheries : a methodology for reconstructing global fishing effort and carbon dioxide emissions, 1950 - 2010
Bibliographic record
Abstract
Whether or not fisheries are sustainable not only affects ocean health, but also human health; a large portion of the population depends on marine ecosystems for food, livelihoods and social values. Our understanding of how fishing impacts the environment is lacking and under the threats of global climate change, the extent to which the ocean can continue to provide goods and services is questionable. Chapter 1 introduces some critical knowledge gaps in fisheries and problems with how marine resources have been managed in the past. Chapter 2 describes a methodology that can be used to quantify and reconstruct historical fishing effort to create a global fishing effort database. Historically fisheries management has not given adequate consideration to the ‘effort factor’, potentially resulting in the mismanagement of marine resources. The methodology was applied to the Exclusive Economic Zones of 9 maritime countries, and preliminary results suggest that, although fishing effort appears to be stabilizing, catch per unit of effort is decreasing. Chapter 3 uses the fishing effort calculated in Chapter 2 to estimate the CO₂ emissions from fishing over time. Fishing is not perceived as an important contributor to greenhouse gas emissions and climate change, despite using fishing vessels that rely on the combustion of fossil fuels (Wilson 1999). As in Chapter 2, the methodology was applied to 9 EEZs. It was found that the CO₂ per unit of catch (CO₂PUC; tonnes) increased, despite increases in fuel efficiency, and the industrial sector emitted 3 times more CO₂PUC than the small-scale sector in 2010. It was estimated that fishing contributes approximately between 2.8 – 5.2% to global CO₂ emissions annually. The final chapter, Chapter 4, discusses the preliminary results of the 9 sample EEZs within the context of the sustainability of fisheries and what it means to be sustainable.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".