Remote monitoring of a deep‐sea marine protected area: The Endeavour Hydrothermal Vents
Bibliographic record
Abstract
Abstract Deep‐sea marine protected areas (MPAs) present particular challenges for management. Their remote location means there is limited knowledge of species and habitat distribution, and rates and scales of change. Yet, evaluating the attainment of conservation objectives and managing the impact of human activities both require a quantitative understanding of natural variability in species composition/abundance and habitat conditions. Ocean Networks Canada (ONC) and Fisheries and Oceans Canada are collaborating in the development of remote monitoring tools for the Endeavour Hydrothermal Vents MPA in the north‐east Pacific. This 98.5 km2 MPA, located 250 km offshore Vancouver Island, encompasses five major fields of hydrothermal vents, at depths of 2200–2400 m. A real‐time cabled observatory was installed at the Endeavour site in 2010. Scientific research for the conservation, protection and understanding of the area is permitted within the MPA and is the primary activity impacting the area. Research activities require the use of submersibles for sampling, surveying and observatory infrastructure maintenance. Data and imagery from remotely operated vehicle dives and fixed subsea observatory sensors are archived in real time using ONC's Oceans 2.0 software system, enabling evaluation of the spatial footprint of research activity in the MPA and the baseline level of natural ecosystem change. Recent examples of database queries that support MPA management include: (1) using ESRI ArcGIS spatial analysis tools to create kernel density ‘heat maps’ to quantify the intensity of sampling and survey activity within the MPA; and (2) quantifying high‐frequency variability in vent fauna and habitat using sensor and fixed camera data. Collaboration between researchers and MPA managers can help mitigate the logistical challenges of monitoring remote MPAs. Recognition at the policy level of the importance of such partnerships could facilitate the extension of scientific missions to support more formal monitoring programmes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".