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Record W2549796458 · doi:10.2172/1136359

Transportation Operations Model (TOM) Technical Manual

2012· report· en· W2549796458 on OpenAlex
Ingrid Busch, Rob L Howard

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.
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

Venuenot available
Typereport
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersCanadian Foundation for Dietetic Research
KeywordsCore (optical fiber)Benchmark (surveying)Barrel (horology)Core modelComputer scienceNuclear engineeringSimulationMechanical engineeringEngineeringTelecommunicationsMathematicsGeology

Abstract

fetched live from OpenAlex

The Transportation Operations Model (TOM) is a component of the Transportation Storage Logistics (TSL) model, which assists the analyst in planning and evaluating scenarios for the transportation and storage of spent nuclear fuel (SNF). TOM was originally developed for the DOE’s Office of Civilian Radioactive Waste Management (OCRWM) in 2006. TOM provides detailed modeling of the transportation of spent nuclear fuel and the assets needed to accomplish that. This Transportation Operations Model (TOM) Technical Manual presents the theory and algorithm behind the model. Further information on the Transportation Storage Logistics model is available in the Transportation Storage Logistics Users’ Manual, and in the Transportation Storage Logistics Model Data Management Manual.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.989

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 head0.034
GPT teacher head0.256
Teacher spread0.222 · 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

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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