Bibliographic record
Abstract
Progress in ocean research is inextricably linked to advances in instrumentation and technology. Modern ocean science research increasingly deals with the dynamic physical, chemical, and biological processes within the oceans and how these processes interact over long time and spatial scales. This type of research requires strong links between scientists conducting the research and others developing the instruments and technology. The National Science Foundation (NSF) provides approximately 70 percent of all funding for basic ocean science research in the U.S. Research activities fall within the four primary ocean science disciplines: biological, chemical, physical oceanography, and marine geology and geophysics. Despite great diversity in observational needs between these diciplines, three general categories of instrument development projects sponsored by NSF reflect distinct community requirements. Demonstration if feasibility projects test an idea for improving existing instrumentation. Goals are readily achievable over a short duration and have modest budgets. Implementation projects are wide-ranging, multi-year projects involving development of new instrumentation. Instrumentation systems development projects integrate a number of observational and operational systems. These require cooperative efforts between scientists and engineers and are both lengthy and expensive. Given the diversity of ocean science activities, important roles exist for federal mission agencies, private and state researchmore » institutions, industry and individuals.« less
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.992 | 0.994 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".