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Record W6948333298 · doi:10.5064/f6buax58/c0lhvx

Burke_EDI_NENA.inventory.text.2017.10.08.tab

2018· dataset· en· W6948333298 on OpenAlexaff

Bibliographic record

VenueSyracuse University Qualitative Data Repository · 2018
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPlant chemical constituents analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsData sourceWorkbookListing (finance)Data fileWeb pageFile formatInformation source (mathematics)Microsoft Office

Abstract

fetched live from OpenAlex

An inventory of text source data files: A data listing that provides an inventory of the text sources used. All text source data files are listed within the data listing. Each unique text data source is listed individually, including date of collection, web browser used, location of source (i.e., web link), file name, and notes regarding translation where applicable. The inventory of text source data files is organized using Microsoft Excel 2016 as an Excel Workbook (.xls, .xlsx) and converted to Comma Separated Values (.csv) and OpenOffice Calc (.ods) file formats. EDI_NENA.inventory.text.yyyy.mm.dd

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.802
Threshold uncertainty score0.663

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1980.270

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 head0.069
GPT teacher head0.296
Teacher spread0.227 · 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
Domainnot available
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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