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Record W1672459893 · doi:10.13039/501100003339

Satellite images and teaching of Geography

2015· article· en· W1672459893 on OpenAlexfundno aff
Javier Martínez Vega, Marta Gallardo, Pilar Echavarría

Bibliographic record

VenueDIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGeography and Education Methods
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónFaculty of Science, Silpakorn UniversityEuropean Social FundRoyal SocietyConsejo Superior de Investigaciones CientíficasOffice of ScienceEuropean Regional Development FundUniversitat de BarcelonaValioJunta de AndalucíaGobierno de AragónFP7 Food, Agriculture and Fisheries, BiotechnologyMinisterio de Economía y CompetitividadNatural Sciences and Engineering Research Council of CanadaFundación EndesaGeneralitat de CatalunyaUniversidade Federal de Santa CatarinaUniversidade de VigoUniversidad de GranadaInstituto de Salud Carlos IIIXunta de GaliciaInner Mongolia UniversityMinistero dell’Istruzione, dell’Università e della RicercaCentro de Investigaciones Energéticas, Medioambientales y TecnológicasConselho Nacional de Desenvolvimento Científico e TecnológicoInstituto de Física de CantabriaUniversity of TabrizUniversidad Nacional de ColombiaCYTED Ciencia y Tecnología para el DesarrolloRoyal Society of ChemistryEusko JaurlaritzaEuropean CommissionUniversitat Jaume IFundação de Amparo à Pesquisa do Estado de São PauloCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorInstituto Nacional de Investigación y Tecnología Agraria y AlimentariaU.S. Department of EnergyPartnership for Research and Innovation in the Mediterranean AreaUniversidad Complutense de MadridRussian Foundation for Basic ResearchUmeå UniversitetMinisterio de Ciencia e InnovaciónComunidad de MadridCentro para el Desarrollo Tecnológico IndustrialGreat Lakes Bioenergy Research CenterEuropean Association of National Metrology InstitutesBanco SantanderRegione CalabriaArab Fund for Economic and Social DevelopmentSilpakorn UniversityCanadian Institute of Steel Construction
KeywordsThematic mapSatelliteResource (disambiguation)Scale (ratio)The InternetGeographySatellite imageryRemote sensingComputer scienceData scienceOrder (exchange)CartographyWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Doctorate Programme in Electrical and Electronics Engineering, Department of Electrical and Electronics Engineering, Universidad del Norte.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.002

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 head0.344
GPT teacher head0.460
Teacher spread0.116 · 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
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

Citations3
Published2015
Admission routes1
Has abstractyes

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