MétaCan
Menu
Back to cohort
Record W33523939 · doi:10.1126/sciadv.abc0671

The problems in a Question Answering system in the academic domain

2007· book-chapter· en· W33523939 on OpenAlexfundno aff
Pilar López Moreno, Antonio Ferrández, Sandra Emilce Roger Calzetti, Sergio Ferrández Escámez

Bibliographic record

VenueScience Advances · 2007
Typebook-chapter
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
FundersH2020 Excellent ScienceEnvironment Canada
KeywordsDomain (mathematical analysis)Work (physics)Government (linguistics)Question answeringLibrary sciencePolitical scienceEngineering managementOperations researchComputer scienceEngineeringInformation retrievalPhilosophyMechanical engineeringLinguisticsMathematics

Abstract

fetched live from OpenAlex

This research has been partially funded by the Spanish Government under project CICyT number TIN2006-15265-C06-01 and by the University of Comahue under the project 04/E062. This work has been partially supported by the EU funded project QALL-ME (FP6 IST-033860).

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)0.014
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0030.005
Scholarly communication0.0090.022
Open science0.0030.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0160.007

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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 designSimulation or modeling
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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicNatural Language Processing TechniquesFrench-language works237,207