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ACELERÓMETROS MEMS EN EL DESARROLLO DE POZOS Y CAMPOS PETROLEROS INTELIGENTES

2017· article· es· W2623244782 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

VenueREVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA · 2017
Typearticle
Languagees
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Sensores instaladosen los pozos permiten un monitoreo continuo de los mismos y de todo un campo productor, pero su costo limita su aplicación en campos maduros,donde la producción es baja y el presupuesto limitado. Sensores tipo MEMS, podrían ser utilizados a una pequeña fracción del costo de otro tipo de sensores. Se muestran los resultados de la utilización del acelerómetro MMA7361L de Freescale TM, con el fin de obtener la posición instantánea de un sistema de bombeo mecánico. La posicióninstantánea de la barra pulida es una de las variables físicas importantes, para obtenerel dinagrama de fondo de pozo,a partir del cual se puede identificar la falla presente en el sistema de bombeo.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0080.001
Research integrity0.0020.002
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 head0.031
GPT teacher head0.301
Teacher spread0.270 · 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