NIRST: a satellite-based IR instrument for fire and sea surface temperature measurement
Bibliographic record
Abstract
NIRST is a pushbroom scanning infrared radiometer that makes use of 512×2 arrays of resistive microbolometers. This instrument comprises mainly two cameras, one operating in the spectral band of 3.4-4.2 μm (band 1) and the other in the bands of 10.4-11.3 (band 2) and 11.4-12.3 μm (band 3). It is intended for the retrievals of forest fire and sea surface temperatures in the Aquarius / SAC-D mission. In this mission the satellite will be launched into a Sun Synchronous polar orbit with an ascending node at 6 PM. This orbit suits the need of discriminating forest fires from solar reflections. NIRST is designed to achieve a spatial resolution of 350 m and a swath width of 180 km at nadir. Its field of view can be steered across track up to 500 km on each side to shorten the revisit time. To measure fire intensity temperatures NIRST will perform multispectral scans of ground area in bands 1 and 2 and the acquired data will be analyzed using a double band algorithm. The microbolometer detectors have been designed to exhibit useful dynamic range for this application. It is projected that the detector response in band 1 saturates only when NIRST scans a 350 m ground pixel of average temperature of 700 K. The use of the data acquired in bands 2 and 3 allows for the retrieval of sea surface temperature by means of the split algorithm technique.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".