MétaCan
Menu
Back to cohort
Record W2062132757 · doi:10.21949/1501510

Freight Transportation in South Dakota: Selected Data from Federal Sources

2010· article· en· W2062132757 on OpenAlexaboutno aff
F Ammah-Tagoe, D Mednick

Bibliographic record

VenueRosa P: A digital library for transportation research (United States Department of Transportation) · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransportation Systems and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsDirectoryTruckGovernment (linguistics)CommodityTransport engineeringBusinessTable (database)DatabaseFinanceEngineeringComputer science

Abstract

fetched live from OpenAlex

Welcome to the State Freight Transportation Profile. This report presents information on freight transportation in South Dakota and is part of a series of reports covering all 50 States. The purpose of the report is to present the major Federal databases related to State freight movements. Along with tables generated for each State, this report gives descriptions of the databases, information on access and formats, and contact points. The database descriptions are based on entries in the Bureau of Transportation Statistics' (BTS) Directory of Transportation Data Sources. This publication provides users with a comprehensive inventory of transportation data sources within the Department of Transportation, other Federal government agencies, U.S. private transportation organizations, and Canadian and Mexican government agencies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.459
Threshold uncertainty score0.913

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0170.049
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.005

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 head0.033
GPT teacher head0.263
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueRosa P: A digital library for transportation research (United States Department of Transportation)Same topicTransportation Systems and InfrastructureFrench-language works237,207