CTD profiles from marine conservation areas and offshore locations around Newfoundland and Labrador (2023–2025)
Bibliographic record
Abstract
This dataset includes Conductivity-Temperature-Depth (CTD) casts from various marine closures and offshore locations in Newfoundland & Labrador, Canada. Two casts from Hermitage Bay are from the same station, taken shortly before and after hurricane Erin past by the coast of Newfoundland. Data was collected using a SBE 19plus V2 seacat CTD and a WET Labs ECO-AFL/FL Fluorometer. Some Data includes dissolved oxygen recorded using a SBE 43 Dissolved Oxygen Sensor. This dataset contains Conductivity-Temperature-Depth (CTD) profiles collected during scientific surveys conducted from 2023 to 2025 in marine conservation areas and offshore environments around Newfoundland and Labrador, Canada. Data were gathered during summer field campaigns aboard the RV Patrick and William under the project “Monitoring and Assessment of Marine Conservation Areas in Newfoundland and Labrador,” funded by the Oceans Management Contribution Program. Sampling sites include four formally designated marine closures (Hawke Channel, Funk Island Deep, Northeast Newfoundland Slope, and Division 3O Coral) as well as additional locations in Hermitage Bay and along the boundary of NAFO divisions 3Ps and 3L. CTD profiles were collected using a SBE 19plus V2 Seacat CTD equipped with a WET Labs ECO-AFL/FL fluorometer. In 2025, a SBE 43 oxygen sensor was added to the configuration. The CTD unit was deployed using a hydraulic winch from the stern of the vessel and operated at a descent rate of 0.5–1 m/s. The instrument sampled at 4 Hz and was held at the surface for two minutes before descent. Standard Sea-Bird data processing routines (v7.26.7) were applied, including conversion, alignment, thermal correction, and derived variable calculations. Further data formatting and quality checks were performed using the `oce` package in R. All files are provided in CSV format.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".