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Record W3030271920

Canadian plans for Thematic Mapper data

2011· article· en· W3030271920 on OpenAlexaboutno aff
Amy Collins, W. M. Strome, F. E. Guertin, D.G. Goodenough

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Image Processing and Photogrammetry
Canadian institutionsnot available
Fundersnot available
KeywordsDigital dataRemote sensingThematic MapperComputer scienceData formatData qualityPixelTelecommunicationsDatabaseData transmissionGeographyComputer hardwareEngineeringSatellite imageryArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

To improve the quality of data obtained from remote sensing satellites, and thereby improve Canada's resource management capability, facilities were to enable them to receive, record, process, distribute and analyze TM and MSS data from LANDSAT-4. The CCT format used was changed to the standard format family to make it compatible with other ground stations. All MSS data are now in that format. The antenna, at Prince Albert were modified to receive TM X-band data and a transcription system was added to convert high density digital tape to CCT format. A bulk processing system is being developed to provide geometrically corrected MSS, TM, and SPOT products. Methods are being investigated for integrating multiple pixel data from different satellites and other sources using a digital image analysis system that is being established. A cost benefits study is also underway.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.984

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 head0.101
GPT teacher head0.245
Teacher spread0.144 · 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 teacher head, not a consensus.

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

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

Citations0
Published2011
Admission routes1
Has abstractyes

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