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Record W2981699587 · doi:10.4095/219779

A Lifetime Radiometric Calibration Record for the Landsat Thematic Mapper

2001· report· en· W2981699587 on OpenAlexaff
Phil M. Teillet, Dennis Helder, Brian L. Markham, J. L. Barker, K J Thome, Ron Morfitt, J R Schott, F D Palluconi

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsThematic MapperRadiometric datingRadiometric calibrationRemote sensingCalibrationRadiometryThematic mapEnvironmental scienceGeographyCartographySatellite imageryPhysics

Abstract

fetched live from OpenAlex

A coordinated effort on the part of several agencies has led to the specification of a definitive radiometric calibration record for the Landsat-5 Thematic Mapper (TM) for its 17-year lifetime since launch in 1984. Analogous work for the Landsat-4 TM, launched in 1982, is in progress. The time-dependent calibration record for Landsat-5 TM has been placed on the same radiometric scale as the Landsat-7 Enhanced Thematic Mapper Plus (ETM+). Once implemented operationally, the lifetime radiometric calibration record for Landsat will facilitate the examination of a continuous, near-global data set at 30-m scale that spans almost two decades.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.010

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 head0.072
GPT teacher head0.269
Teacher spread0.197 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations12
Published2001
Admission routes1
Has abstractyes

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