MétaCan
Menu
Back to cohort
Record W6986189392

Oral History Interview with Alva Hudson, December 26, 2007

2007· article· en· W6986189392 on OpenAlexaboutno aff

Bibliographic record

VenueThe Portal to Texas History (University of North Texas) · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyNavyDutyNova scotiaWorld War II
DOInot available

Abstract

fetched live from OpenAlex

The National Museum of the Pacific War presents an interview with Alva Hudson. Hudson joined the Navy in January of 1942. He served in the Armed Guard, using the 20mm gun aboard the tanker SS John Archibald. They transported oil to Halifax, Nova Scotia, across the North Atlantic to Glasgow, Scotland. They traveled with a convoy of around 40 ships. Beginning November of 1943 Hudson served as a gunner on the 40mm gun aboard the USS New Jersey (BB-62). They traveled to Ellice Island. They participated in battles at Majuro Island, Palau, Saipan, Okinawa, Formosa and the Philippine Islands. Hudson completed 36 months sea duty and provides vivid details of his experiences aboard the ships and in the battles. He was discharged in January of 1946.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.393

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1180.022

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 head0.015
GPT teacher head0.213
Teacher spread0.199 · 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 designQualitative
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Portal to Texas History (University of North Texas)Same topicMachine Learning in BioinformaticsFrench-language works237,207