The Fishing Vessel Ocean Observing Network (FVON): Towards advancing collaborative observations for the sustainability of our oceans
Bibliographic record
Abstract
As climate change advances, there is increasing pressure to better understand and monitor the ocean. Oceanographic data play a crucial role in shaping policies and decision-making, from ecosystem-based fisheries management to preparing for extreme events. Improvements in these areas are essential for safeguarding the livelihoods, food security, and safety of coastal communities worldwide. Although there have been significant advancements in monitoring essential ocean variables, critical data gaps remain, limiting both short-term forecasts and long-term predictions. Paradoxically, more subsurface data is available from the open ocean than from nearshore areas (Van Vranken et al. 2023). Coastal, shelf, and boundary regions are under-observed due to the challenges of deploying traditional autonomous or free-drifting ocean observing platforms in these dynamic environments. However, this same dynamism often attracts fish—making these areas vital for fishing. The spatio-temporal ocean data gaps often coincide with fishing activities. This fortuity presents a tremendous complementarity with existing networks with the opportunity to transform ocean observation. Fishing vessels can serve as platforms for a range of oceanographic instruments, enabling cost-effective and scalable data collection. Many types of fishing gear already profile the water column, providing an opportunity for attached sensors to gather valuable subsurface data along the ride. By coupling precise fishing location data with environmental measurements, the data can then optimize physical models of ecosystem dynamics and improve meteorological forecasting. Integrating cost-effective data collection with fishing activities is also an intrinsically inclusive approach to ocean observing. The unique collaboration empowers non-traditional stakeholders to adopt innovative solutions for improved sustainability, profitability, and resilience in their own communities. In addition, the cost-effective implementation allows for unprecedented global expansion, especially into historically underserved geographies. To maximize these benefits and complement existing ocean observing networks, the Fishing Vessel Ocean Observing Network (FVON) has been established as an emerging network within the Global Ocean Observing System, facilitating global impact through local collaborations. Intensive co-design with fishers from various horizons—ranging from industrial vessels to Indigenous artisanal canoes and extending from the equator to polar regions—has outlined the need for FVON to coordinate common standards for technology and deployment, establish best practices, standardize data flows, and facilitate observation uptake across programs. Through these activities, FVON seeks to achieve its mission: to foster collaborative fishing vessel-based observations, democratize ocean observation, improve ocean predictions and forecasting, promote sustainable fishing practices, and facilitate a data-driven blue economy.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".