MétaCan
Menu
Back to cohort
Record W7057996779

Lurie, Robert: San Francisco Giants

2020· article· en· W7057996779 on OpenAlexaboutno aff

Bibliographic record

VenueScholarly Commons (University of the Pacific) · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsGloomPretextLimitingNucleofectionParaphernaliaCircumstantial evidence
DOInot available

Abstract

fetched live from OpenAlex

Bob Lurie: It started out when Chuck Feeney, who was my big buddy and was at that time president of the National League, said the Orange want to have a conference call and talk about the situation. So we had a conference call – I think it was on a Monday or so at noon. I had just come back from a trip through Minneapolis because a Short had fallen down and broken his hip and was in the hospital. We had the conference call at noon and I requested 48 hours to find a partner to try to buy the Giants. And in their great wisdom, they gave me five hours. Walter O’Malley was a great help in that he didn’t want to see the rivalry disappear by having the Giants going to Toronto. So he tried to get a longer period, but they said, “No, call us back in five hours.” Back then there were a lot of people, supposedly, interested in getting involved. As I tried to call some of those people, it turned out they were all out to lunch for more than five hours. So nobody was around until I guess Corey got a call from a man by the name of Bud Herseth -- probably around 3 o’clock that afternoon -- saying that he loved baseball, heard there was something going on in San Francisco and a problem. Bottom line to several phone calls in an hour or two was that he had some money, he wanted to put it up, he wanted to become a partner, and we verbally came to an agreement. At 5 o’clock that day, I called back and said I had a partner. Buzzy Vuvezy said, “Oh, I know about Herseth. He’s a good person; he’d fit in fine.”, so they gave us approval, and things went from there.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.929

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0720.000

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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueScholarly Commons (University of the Pacific)Same topicMagnetic confinement fusion researchFrench-language works237,207