A Quantitative Analysis of Greenhouse Gas Emissions from the Korean Offshore Large Purse Seine Fishery
Bibliographic record
Abstract
Ozone layer depletion and global warming related to GHG (greenhouse gases) emissions from industries are a major issue globally. As these efforts, The parties of the Kyoto protocol adopted in the 3th UNFCCC’s conference set targets for average 5.2 percent reduction of GHG emissions from 1990 until 2012, should apply greenhouse gas emissions trading. The 18th UNFCCC’s conference of the parties to be held in Doha, Qatar agreed the Doha amendment to extend the Kyoto protocol that expires in 2012 until 2020. Furthermore, GHG emissions from the fishery industries also represent an important issue, as indicated by Responsible Fisheries at Cancun, Mexico, in The 16th UNFCCC’s conference of the parties, United nations conference on environment & development accepted Responsible Fisheries as important concern area. However, few research on the GHG emissions from Korean fisheries have been performed. Therefore, a quantitative analysis of GHG emissions from the major Korean fisheries in needed before guidelines for reducing GHG emissions from the fishing industry can be established. The aim of this study was to assess the present GHG emissions from the Korean offshore large purse seine fishery using the Life Cycle Assessment (LCA) method quantitatively. The result of this study will be helpful to establish a reducing method of GHG emissions.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".