GHRSST NOAA/STAR Metop-A AVHRR FRAC ACSPO v2.80 0.02 L3U Dataset (GDS version 2)
Bibliographic record
Abstract
This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-A AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MA-STAR-L2P-v2.80 and in Pryamitsyn et al. (2021).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.046 |
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".