MétaCan
Menu
Back to cohort
Record W7070123925

Oral History Interview with Bud Rohling, May 18, 2006

2006· article· en· W7070123925 on OpenAlexaboutno aff

Bibliographic record

VenueThe Portal to Texas History (University of North Texas) · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyPacific oceanService (business)World War IIEast coastUnit (ring theory)China
DOInot available

Abstract

fetched live from OpenAlex

The National Museum of the Pacific War presents an oral interview with Bud Rohling. Rohling recalls volunteering for the service shortly after Pearl Harbor was attacked. He was called up in March, 1942 and went into flight training. After training, he was assigned to the 3rd Photo Reconnaissance Squadron. Rohling's first job was to fly over the coast of northern Canada and Alaska and take photographs. He mentions also flying over the coast of Russia and taking a few photographs there as well. From there, he was assigned to Gush Kara, India. Rohling's unit ferried fuel to China and they flew photo recon missions along the coast. They did that for seven months and then went back to McDill Air Force Base, Florida. Once he returned, Rohling was assigned to B-29 bombers. His next assignment was on Saipan where he ran photo recon missions over the home islands of Japan. Rohling describes participating in some fire bombing missions over Japan. Rohling recalls photographing the atomic attack on Nagasaki. When the war ended, Rohling had enough points to rotate home, but instead made a request to join General Curtis LeMay's headquartes staff and hopefully stay in the Marianas. He ended up in Tokyo making more reconnaissance flights before being transferred to the Philippines. At Manila, he separated from the service and started up a small airline compnay in the Philippines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Portal to Texas History (University of North Texas)Same topicGenetics, Bioinformatics, and Biomedical ResearchFrench-language works237,207