MétaCan
Menu
Back to cohort
Record W1971899740 · doi:10.3141/2255-06

Problem of Transshipment in Travel Forecasting

2011· article· en· W1971899740 on OpenAlexfundaboutno aff
William J Melendez, Alan J. Horowitz

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2011
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
FundersMinistère des TransportsU.S. Department of Transportation
KeywordsTransshipment (information security)TruckCommodityTransport engineeringOperations researchComputer scienceIdentification (biology)BusinessEngineering

Abstract

fetched live from OpenAlex

The concept of the transshipment of goods has not been widely incorporated into models for transportation planning. A model with transshipment should recognize that a whole shipment could be transported in two or more stages involving intermediate points (transshipment points) between the origin and the final destination. The database containing data from the Ontario, Canada, Commercial Vehicle Survey is one of the few databases that contains substantial transshipment information. The analysis of the Ontario Commercial Vehicle Survey first focused on commodities and their origin–destination facilities and defined terminals and warehouses as possible transshipment locations. Analysis revealed that any commodity was likely to be transshipped through either a truck terminal or a warehouse. Eight tour structures could be ascertained from the database, with each structure differing in the order and number of transshipment points and previous customers. A choice model of those tour structures was built. Factors such as commodity type, origin–destination facility type, truck type, distance, and shipment size were significant, depending on the structure.

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.

How this classification was reachedexpand

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.242
GPT teacher head0.325
Teacher spread0.083 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicUrban and Freight Transport LogisticsFrench-language works237,207