MétaCan
Menu
Back to cohort

Diagnóstico de la condición de desgaste basado en el análisis de aceite usado. Caso de estudio: Vehículo de servicio de taxi

2020· article· es· W3092776573 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Las condiciones operativas del motor de un vehículo, dependen del combustible, lubricante y los componentes, para ello se plantea el diagnóstico directamente por un monitoreo continuo del lubricante. Este proyecto evalúa la condición de desgaste del motor por encendido provocado en un vehículo de uso público tipo taxi, aplicando la técnica de análisis de lubricante usado.El proceso de investigación es experimental, se levanta una línea base del lubricante y vehículo, para la toma de muestras cada 4000 kilómetros de recorrido, en cada muestra se analiza las propiedades físicas y químicas del lubricante, se establece el comportamiento de la viscosidad, partículas contaminantes y partículas metálicas del MEP del vehículo. El comportamiento del índice PQ y partículas metálicas, muestra una tendencia constante para el desgaste, el índice PQ combinado con un bajo número ppm de hierro indica una tendencia de desgaste normal, también, se descarta la contaminación por el ambiente que rodea el funcionamiento del MEP, indicativo de una incidencia leve para desgaste abrasivo en las piezas móviles del motor.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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