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Record W313882354 · doi:10.1021/es060989a

Remote Sensing of in Use Heavy Duty Diesel Vehicles

2006· article· en· W313882354 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

VenueSound Ideas (University of Puget Sound) · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Detection Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHeavy dutyDiesel fuelEnvironmental scienceAutomotive engineeringWaste managementBusinessEngineering

Abstract

fetched live from OpenAlex

On-road measurements in 2005 of carbon monoxide (CO), hydrocarbons, nitric oxide, nitrogen dioxide, and sulfur dioxide from 1641 individually identified heavy-duty diesel trucks at two locations in Colorado are reported. Carbon monoxide and nitric oxide show increasing emissions with increased altitude. Oxides of nitrogen (NOx) emissions have decreased with more recent model years over the last 10 years but are the same as vehicles that are 20 years old. At the Golden, CO site, there was a statistically significant decrease in fleet emissions of CO and NOx since a similar study in 1999. There was no emission trend for CO or NOx with gross vehicle weight or odometer in units of grams of pollutant per kilogram of fuel consumed. Data from this study suggest that on-road remote sensing can detect illegal, high sulfur fuel use from individual heavy-duty diesel trucks. Ammonia emissions from this study were below the detection limit of the instrument but will be useful as a baseline value for future comparison.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.677

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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