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Record W4396508832 · doi:10.22215/etd/2024-15900

Comparing Machine Reading Substitution To Alternative Statistical Treatment Methods For Left-Censored Data: Considerations To Limit Bias In Maternal-Infant Research On Environmental Chemicals (MIREC) Study

2024· dissertation· en· W4396508832 on OpenAlexafffund
Alicia De Las Mercedes Ward

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMathematics
TopicStatistical Methods and Bayesian Inference
Canadian institutionsCarleton UniversityInstitut National de Santé Publique du Québec
FundersHealth Canada
KeywordsReading (process)Substitution (logic)Limit (mathematics)StatisticsEconometricsComputer scienceMathematicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Comparing Machine Reading Substitution To Alternative Statistical Treatment Methods For Left-Censored Data: Considerations To Limit Bias In Maternal-Infant Research On Environmental Chemicals (MIREC) Study

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)0.379
metaresearch head score (Gemma)0.647
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.997
Threshold uncertainty score0.766

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3790.647
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0020.003
Science and technology studies0.0020.005
Scholarly communication0.0040.007
Open science0.0060.004
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0100.001

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 head0.458
GPT teacher head0.572
Teacher spread0.114 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSimulation or modeling
DomainMethods
GenreMethods

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
Published2024
Admission routes2
Has abstractyes

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