Seafloor Mapping Puzzle―Where Do You Fit?
Bibliographic record
Abstract
Understanding our oceans is essential to predictions that will help guide sustainable development of the seafloor and the water above it, as well as guiding human adaptation to inevitable change. The most fundamental ocean observation is a measured map of the seafloor; without it other ocean observation models are limited. To map the seafloor successfully, cooperation and collaboration from governments, universities, nongovernmental organizations, maritime industries, and citizens are key. These stakeholders must work together, overcoming internal institutional inertia or distrust of novel types of partnerships, to either consolidate existing data, share data that are currently not in the public domain, help map areas where no data exist, or just help get the message out that these data are needed. It is imperative that we shift to a “collect and share” mindset, allowing data to live beyond our own immediate needs and serve the maximum good. When we do this, together we will move ocean science forward and meet our shared goal of a healthy, sustainable ocean for generations to come. Presenter Bio Kelley Brumley is a marine geologist and works as the Science Manager of Ocean Mapping for Fugro, in Houston, Texas. She is an adjunct professor at University of Houston and Affiliate Faculty at University of Alaska Fairbanks (UAF). She received an M.S. in geology from UAF and a Ph.D. from Stanford University. Between 2006-2012, she was a member of the science party during the U.S. and Canada’s Extended Continental Shelf mapping efforts in the Arctic Ocean. Since joining Fugro in 2014, Dr. Brumley has acted as lead scientist on many regional multibeam mapping and geochemical coring surveys investigating cold seep locations and related chemosynthetic habitats. In her current role she supports the development and execution of Fugro’s Sustainability strategy which includes participation in Seabed 2030 working groups, planning around the U.N. Decade of Ocean Science for Sustainable Development, and expansion of Fugro’s crowdsourced bathymetry program.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".