Societal Applications in Fisheries and Aquaculture Using Remotely-Sensed Imagery - The SAFARI Project
Bibliographic record
Abstract
The principal objective of the SAFARI (Societal Applications in Fisheries and Aquaculture using Remotely-Sensed Imagery) initiative is to coordinate, at the international level, applications of remotelysensed Earth Observation data to the societal benefit areas of fisheries and aquaculture.Applications of remote sensing to fisheries include its use in i) fish stock assessment, where earth observation data is instrumental in the understanding of the effect of seasonal and inter-annual variability of the phytoplankton community to stock growth and recruitment, ii) in fisheries harvesting by identification, for example, of potential fishing zones, and iii) more generally in fisheries management, where the concept of the ecosystem-based approach is now universally accepted and introduced in the regional management of fisheries.Remotely-sensed Earth observation data are also useful in fisheries management for monitoring water quality and Harmful Algal Blooms (HAB) in coastal waters, helping with the protection of endangered species and creation of marine protected areas.To achieve its main objective, the SAFARI initiative endeavours to reach all levels of participants engaged in global fisheries research and management, including policy makers, research scientists, government managers, and those involved in the fishing industries.SAFARI activities include organization of international workshops and symposia as a platform to discuss current research in Earth observation and fisheries management, information sessions aimed at the fisheries industry, government officials and resource managers, representation at policy meetings, and producing publications relevant to the activities.Researchers from more than 15 countries including India, Japan, Sri Lanka, France, UK, Spain, Italy, Canada, USA, Australia, New Caledonia, Peru, Argentina, Columbia and South Africa, are part of the SAFARI network, have attended workshops organized by SAFARI and contributed to a monograph on the topic to be published by the IOCCG (International Ocean-Colour Coordinating Group).Moreover, collaboration with other international networks, such as ChloroGIN (Chlorophyll Global Integrated Network) will facilitate the expansion of SAFARI worldwide.This community white paper, relevant to theme three of the conference, provides a portrait of the current and potential benefits of remotely-sensed data to society, and more specifically to management of fisheries and aquaculture at the global scale.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".