Bibliographic record
Abstract
A digital acoustic seabed classification system, QTC View (Series IV) was used in the coastal waters of Newfoundland to characterize and classify marine benthic habitats. The QTC View system was calibrated in Placentia Bay at sites identified independently during a submersible research program. Four different habitats were used for calibration of the QTC View system: mud, gravel, rock, and macroalgae on rock. These different habitats were used as a “training” catalogue for real-time classification of marine habitats carried out in Bonavista Bay. The classification data were based on over 2000 km of survey tracks ranging in depth from approximately 10-m to 220-m depth. Post classification analyses were carried out using data visualization techniques, simultaneously comparing the classification data in mathematical and geographic settings. Following post classification, eight different marine habitats were identified using the acoustic system: mud, loose gravel, gravel, rock, sparse algae/cobble, macroalgae, high relief/deep cobble, and wood chips. Throughout the surveyed area, rock habitat dominated, followed by sparse algae/cobble and high relief/cobble habitat types. The wood chip habitat type was identified within a small area that historically had been associated with logging in coastal Newfoundland.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".