Bibliographic record
Abstract
Instrumentation used for limnology, the studyof our world's lakes, is often developed foroceanography and then gradually crossedover as new technologies gain widespreadacceptance in the ocean community. Autonomousunderwater vehicles (AUVs) have been widelydeployed in naval operations, oil and gassurveys, and scientific studies with limitedfreshwater application. UBC-Gavia, aGavia-class AUV owned and operated bythe Environmental Fluid Mechanics group atthe University of British Columbia, has beendeployed as a monitoring and data collectionplatform for lake bottom and water columnsurveys alike.Untethered, AUVs are a powerful tool asthey can often travel to regions that would belogistically difficult or otherwise impossibleto access using more traditional surface basedsurvey tools (e.g. towed sonar arrays, ROVs,profilers, etc.). These vehicles are well suitedto polar exploration as they can be deployedfrom the ice surface with relatively lowinfrastructure cost and provide significantamounts of information on water bodies ofwhich there tends to be a dearth of collecteddata. This is essential if mankind is to betterunderstand and monitor the widespreadimpact that climate change is bringing toour Polar Regions. In the past two decades,several through-ice AUV surveys have beenconducted; however, UBC-Gavia was the firstAUV to be used in an under-ice limnologicalstudy. The three case studies presented heredemonstrate UBC-Gavia as an importantplatform for exploring our freshwaterenvironments with and without ice cover:(1) Loch Etive, Scotland; (2) Lake Ontario,Canada; and, (3) Pavilion Lake, Canada.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".