Using blockchain and AI technologies for sustainable, biodiverse, and transparent fisheries of the future
Bibliographic record
Abstract
This paper proposes a total fusion of blockchain and AI tech for tomorrow’s viable, rich in diversity and transparent fisheries. It outlines the main goal of tackling overfishing challenges due to lack of transparency and biodiversity depletion in the fisheries sector. With the use of blockchain technology, we can ensure that all fishery products are safely traced from their harvest up to when they get to the market— at the same time, AI algorithms are used in monitoring fish populations and predicting them plus decision-making processes which should be enhanced thus promoting bio-diversity and ensuring sustainability of fish stocks. Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations. The conclusion underscores the transformative nature of these technologies as having great implications towards fisheries management; this implies that there is a need for future observational studies aimed at validating such other findings.
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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 | 0.005 |
| 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 | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".