MétaCan
Menu
Back to cohort
Record W4246282778 · doi:10.1108/oxan-db220378

South-east Asian port efficiency challenges loom

2017· other· en· W4246282778 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2017
Typeother
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsLaggingPort (circuit theory)Investment (military)BusinessEast AsiaLOOMQuarter (Canadian coin)Supply chainTransport engineeringEconomyChinaEngineeringGeographyEconomicsMarketingPolitical science

Abstract

fetched live from OpenAlex

Subject Port and shipping infrastructure in South-east Asia. Significance South-east Asia is likely to experience a recovery in freight shipping this year, after cargo traffic volumes grew in the fourth quarter of 2016. However, this rebound will put further pressure on congested ports and the region’s logistical capability. Impacts Port congestion should ease as more capacity comes online, but secondary gateways will see little improvement. Regulatory reforms are lagging expansion plans, depriving the industry of much-needed investment. More investment will be needed in road and rail networks to link ports to supply chains.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.113
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0090.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1130.020

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 head0.023
GPT teacher head0.230
Teacher spread0.207 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEmerald expert briefingsSame topicMaritime Ports and LogisticsFrench-language works237,207