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Record W2086032978 · doi:10.1029/2004eo020007

New global drifter data set available

2004· article· en· W2086032978 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEos · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsDrifterBuoyRaw dataSatelliteEnvironmental scienceMeteorologyNavyOceanographyTable (database)Data setClimatologyGeographyComputer scienceGeologyDatabaseLagrangianEngineering

Abstract

fetched live from OpenAlex

Since 1978, oceanographers, meteorologists, and the U.S. Navy have deployed a large number of Argos satellite‐tracked drifters in all of the major ocean basins (Table l). The Data Buoy Cooperation Panel (DBCP) of the World Meteorological Organization/Intergovernmental Oceanographic Commission (WMO/IOC) has coordinated the deployment of drifters via cooperative projects in various ocean basins. In any given month since 1993, there has been an array of more than 600 drifters in the global ocean (http://www.aoml.noaa.gov/phod/dac/ dacdata.html). Most of the raw observations and processed data have been accumulating at the Meteorological and Environmental Data Service (MEDS), Canada. The raw data on file have been processed from MEDS, and other sources, and merged with the processed data at the Atlantic Oceanographic and Meteorological Laboratory (AOML) to form a single file.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.031
GPT teacher head0.234
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it