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NOx Emissions Reduction Through Water Injection

2006· article· en· W2141416413 on OpenAlex
Ernst Radloff

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.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueNaval Engineers Journal · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
FundersTransport Canada
KeywordsNOxEnvironmental scienceGreenhouse gasEnvironmental engineeringEngineeringWaste managementOceanographyChemistry

Abstract

fetched live from OpenAlex

Abstract Transport Canada is committed to protect the environment and to achieve a more sustainable transportation system. The Transportation Development Centre of Transport Canada, in collaboration with the Environment Research and Measurement Division of Environment Canada, is conducting research on marine emissions control technologies to lower greenhouse gas (GHG) emissions. » The ongoing work on emissions control technologies comprises both laboratory and field trials. An initial round of laboratory tests demonstrated the viability of a Water Injection System (WIS) to reduce NOx emissions. The results indicate that the WIS has achieved a 20 to 28% NOx reduction over a 25 to 75% load range with a maximum of 33% water injection. The WIS was optimized during laboratory bench tests and will be installed on a RORO vessel operating between Montreal, Quebec, and St. John's, Newfoundland. The WIS provides a cost‐effective solution for NOx reduction on Canadian vessels fitted with older medium‐speed engines.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.992

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)0.0090.000

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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