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Record W2564747207 · doi:10.5539/jpl.v10n1p188

Comparative Study of Bill of Lading Function as Title Document

2016· article· en· W2564747207 on OpenAlexvenueno aff
Majid Reza Arab Ahmadi, Mostafa Elsan, Ibrahim Noshadi

Bibliographic record

VenueJournal of Politics and Law · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsBill of ladingReceiptScope (computer science)CarriageBusinessFunction (biology)LawCommerceLaw and economicsEconomicsComputer scienceAccountingPolitical scienceEngineeringInternational trade

Abstract

fetched live from OpenAlex

One of the new transformations in the scope of maritime transportation about shipping documents includes non-paper (electronic) maritime transport documents. Three principal functions of a bill of lading in maritime transportation comprise receipt of carriage of good, reason for carriage contract, and title document. The majority of studies are about to realize the functions of carriage receipt and reason for carriage contract within substitution of electronic documents, such as replacement of paper bill of lading with an electronic bill of lading. While there is not the same conclusion about the function of the title document and this doubt on the result is beyond the replacement of paper documents with electronic documents caused by ambiguities in considering the bill of lading as a title document of goods and function of a bill of lading as a title document. In general, the transfer of a bill of lading does not mean the transfer of ownership while ownership is transferred through a contract of goods’ sale and bill of shading is considered as a document that indicates a property of goods to facilitate transactions within commercial operations.

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)0.003
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.002

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 head0.043
GPT teacher head0.290
Teacher spread0.247 · 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
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

Citations5
Published2016
Admission routes1
Has abstractyes

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