STAR NOAA HRPT Satellite Data. Version 1.0
Bibliographic record
Abstract
The NOAA Polar Orbiting Environmental Satellite (POES) satellite platforms provided High Resolution Picture Transmission (HRPT) image data products. The HRPT consists of six channels of Advanced Very High Resolution Radiometer (AVHRR) data in the visible, near-infrared and infrared spectrum. The data are digitized to 10-bit precision and transmitted at 360 Lines Per Minute (LPM) at 665 kilobits per second (kbps). The HRPT data stream also included non-imagery data from other instruments on board the spacecraft. The AVHRR instrument has a resolution of 1.1 km providing 10-bit data in five separate spectral bands. Contrasted with the 4 km, 8 bit data and 2 spectral bands for the Automatic Picture Transmission (APT) for low resolution imagery transmissions, HRPT, with its 5 data channels and 10 bits of data represents about a 10-fold advantage in the amount of information that may be analyzed when compared to APT data. This is very important to meteorologists and other professionals who need the most accurate information available for analysis especially in a region of limited satellite coverage. The HRPT satellite images were uploaded to the Committee on Earth Observation Satellites (CEOS) ftp site by Ed Hudson during STAR period. The images cover the STAR area only. A script file was set to download the data once there were new data posted. For further information on HRPT imagery, please see: https://noaasis.noaa.gov/POLAR/HRPT/hrpt.html
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.060 |
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".