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Record W318904654

Get Ready for a Rough One: While the Road Industry Looks Steady for 2008, Other Construction Sectors and the Overall Economy Look Shaky at Best

2008· article· en· W318904654 on OpenAlexaboutno aff
K Landers

Bibliographic record

VenueBetter roads · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Bridge (graph theory)BusinessTransport engineeringFinanceEconomicsEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

This article looks back at budget trends for the road industry in 2007, and predicts flat or slightly negative growth overall, except in the southern and western states and Canada. More than a third of state Departments of Transportation expect modest budget increases of two percent or better in 2008, but a quarter anticipate budget cuts of at least two percent. Township agencies and Canadian agencies expect larger budgets in 2008 and none anticipate budget cuts. Budget trends emphasize bridge maintenance repair, pavement maintenance, bridge deck repair and traffic safety. Highway and bridge contractors in North America did a better business than they had anticipated, but again it differed by region. For 2008, about one-third of contractors polled expect above average business, while about a quarter expect a below-average year. A number of charts and graphs illustrate these findings more thoroughly.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.040
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0070.008
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0290.013

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 head0.040
GPT teacher head0.219
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2008
Admission routes1
Has abstractyes

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