Bibliographic record
Abstract
Abstract Northwest Atlantic current systems originating off Greenland extend south to the Canadian Maritimes and Northeastern United States, creating oceanographic, ecological, and economic connections that compel integrated ocean observing across the region. For more than a decade, NERACOOS has led development of a robust and responsive ocean observing system for the Northeastern U.S. as part of the U.S. Integrated Ocean Observing System (IOOS) and Marine Biodiversity Observation Network (MBON), components of the Global Ocean Observing System (GOOS). That experience, backed by key partnerships that reach into northern latitudes, positions us to build new partnerships toward integration of ocean observing at scale in the Northwest Atlantic. Strategic deployment of observing tools should be tailored to local conditions, with oceanographic models, satellite remote sensing, and data products unifying the system at scale. Indigenous people must be core partners, both as contributors of traditional knowledge and priority communities for capacity development. The diversity and complexity of human, environmental, and data systems calls for application of artificial intelligence and machine learning tools to extract key insights from disparate information sources. Longevity will be promoted by involvement of the private sector to build buy-in, and training of young practitioners to sustain the system into the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".