Ocean outfall mapping using an Autonomous \nUnderwater Vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ocean outfalls are difficult to observe and the \ntraditional monitoring methods are expensive and can only \nprovide limited information. As an alternative, Autonomous \nUnderwater Vehicles (AUVs) have proved to be an effective tool \nfor outfall mapping. This paper describes an outfall mapping \nmission by the MUN Explorer AUV off the east coast of Canada. \nA submerged freshwater outfall with Rhodamine WT dye was \ndischarged into a bay and the MUN Explorer AUV equipped with \na fluorometer was used to measure the dye concentration and the \nextent of the dispersed plume. The results have shown that the \nAUV can be effectively used to map the outfall and future work is \nneeded to provide more detailed plume information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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 it