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Record W2389864504

Discussion on a long English abstract in the scientific paper of agricultural journals

2015· article· en· W2389864504 on OpenAlexaff
Liu Yiche

Bibliographic record

VenueBianji xuebao · 2015
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsScience North
Fundersnot available
KeywordsAgricultureAgricultural economicsPolitical scienceGeographyEconomicsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The structural and length characteristics of abstract in the scientific paper of 20 th domestic and foreign agricultural journals are investigated. Our survey shows the abstract of foreign agricultural journals are usually reportorial,and most of domestic agricultural journals also adopt it. Structured abstract is used in two kinds of domestic agricultural journals. A brief abstract is always used in domestic and foreign agricultural journals, but a long English abstract is used in three kinds of domestic journal. We suggest that the structural and length characteristics of abstract in the scientific paper was accorded with the content of article,and a scientific paper in domestic agricultural journals should need a brief Chinese abstract and a long English abstract.

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)0.052
metaresearch head score (Gemma)0.181
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.181
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.013
Science and technology studies0.0050.004
Scholarly communication0.0130.011
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.006

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 head0.053
GPT teacher head0.319
Teacher spread0.266 · 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.

Study designNot applicable
DomainReporting
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
Published2015
Admission routes1
Has abstractyes

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