MétaCan
Menu
Back to cohort
Record W4253891836 · doi:10.4095/219993

Detection of Spectral Line Curvature in Imaging Spectrometer Data

2003· report· en· W4253891836 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCurvatureSpectrometerImaging spectrometerLine (geometry)Spectral imagingPhysicsOpticsRemote sensingGeologyGeometryMathematics

Abstract

fetched live from OpenAlex

A procedure has been developed to measure the band-centres and bandwidths for imaging spectrometers using data acquired by the sensor in flight. This is done for each across-track pixel, thus allowing the measurement of the instrument's slit curvature or spectral 'smile', The procedure uses spectral features present in the at-sensor radiance which are common to all pixels in the scene. These are principally atmospheric absorption lines. The band-centre and bandwidth determinations are made by correlating the sensor measured radiance with a modelled radiance, the latter calculated using MODTRAN 4.2. Measurements have been made for a number of instruments including Airborne Visible and Infra-Red Imaging Spectrometer (AVIRIS), SWIR Full Spectrum Imager (SFSI), and Hyperion. The measurements on AVIRIS data were performed as a test of the procedure; since AVIRIS is a whisk-broom scanner it is expected to be free of spectral smile. SFSI is an airborne pushbroom instrument with considerable spectral smile. Hyperion is a satellite pushbroom sensor with a relatively small degree of smile. Measurements of Hyperion were made using three different data sets to check for temporal variations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.643

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)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.270
Teacher spread0.231 · 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