Identification and Characterization of Atlantic Halibut (Hippoglossus hippoglossus) Habitats with Bathymetric Data Supplemented with Seafloor Video Observations
Bibliographic record
Abstract
The utilization of first-order information about seafloor morphology, derived from multibeam sonar data, has become common in the investigation of deep-sea benthic habitats. When combined with complementary datasets, these data can be used to study deep-sea coral ecosystems and predict environments that are favorable for fish spawning, larval nurseries, and juvenile fish habitats. The identification and protection of these environments is critical where biodiversity is vulnerable or unique in order to rehabilitate or maintain ecological communities and encourage higher fecundity. In August of 2019, the expedition Deep Connections: Exploring Atlantic Canyons and Seamounts was conducted to explore understudied deep-sea environments aboard the NOAA Ship Okeanos Explorer off the coast of the United States and Canada (EX1905L1 and EX1905L2). This expedition included multibeam mapping and seafloor exploration with a Remotely Operated Vehicle (ROV). Observations from ROV dives include several fish species including multiple sightings of Atlantic halibut (Hippoglossus hippoglossus), which is considered endangered on the International Union for Conservation of Nature Red List of Threatened Species. Identifying and classifying the habitats where Atlantic halibut is observed would facilitate future endeavors of protection or rehabilitation. Spawning events are known to coincide with areas of increased seafloor slope associated with high energy systems such as canyons. Utilizing multibeam data included in the Global Multi-Resolution Topography (GMRT) Synthesis, we characterize canyons at the edges of George’s and Brown’s Banks based on morphology, roughness, and seafloor slope and aspect. We combine these data with observations of Atlantic halibut from ROV video to seek correlations that can be used to identify potential habitats. This information can be used to guide further exploration and characterization of the seafloor to better understand the spatial extent of Atlantic halibut habitat in the region.
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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 | 0.000 |
| 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 | 0.000 |
| 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".