Deep-Sea Exploration with the E/V Nautilus: 2015 Season Highlights and Upcoming Opportunities
Bibliographic record
Abstract
The 2015 Exploration Vessel Nautilus season spanned six months and two oceans. From the Gulf of Mexico to the Pacific Ocean, this presentation will highlight recent exploration of the Galapagos Rift and Platform and the California Borderland, the development of new cutting edge technologies, and the use of telepresence to further our scientific and educational goals. It will also include information on how students, faculty, and others can become involved in the 2016 Nautilus expedition. Presenter Bio Nicole Raineault is the Director of Science Operations for the Ocean Exploration Trust (OET). She has been working with the OET aboard the E/V Nautilus since 2009 in many capacities including Navigator, Data Manager, Expedition Leader, and Chief Scientist. Nicole is a marine geologist with degrees in marine science (University of Maine, B.S.), oceanography (Rutgers, M.S.), and geology (University of Delaware, Ph.D.). She completed her post-doctoral fellowship at the University of Rhode Island Graduate School of Oceanography in 2014. Her research interests include seafloor sediment morphology and use of combined technologies to characterize seafloor geology and habitat.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".