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Record W2331015456 · doi:10.1175/2009bams2767.1

Education

2009· article· en· W2331015456 on OpenAlexaboutno aff
John W. Nielsen‐Gammon, Lourdes B. Avilés, Everette Joseph

Bibliographic record

VenueBulletin of the American Meteorological Society · 2009
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPoint (geometry)Class (philosophy)PsychologyMedical educationComputer scienceMathematicsMedicine

Abstract

fetched live from OpenAlex

The AMS's Board on Higher Education undertook a survey of atmospheric science graduate programs in the United States and Canada during the fall and winter of 2007–08. The survey involved admission data for the three previous years and was performed with assistance from AMS headquarters and in cooperation with the University Corporation for Atmospheric Research (UCAR). Usable responses were received from 29 programs, including most major atmospheric science programs. The responding schools receive between 6 and 140 applications per year, and typical incoming class sizes range from 1 to 24. About 69% of applicants and 76% of enrollees are domestic students. At the majority of schools, all incoming students receive full financial support. The average graduate program looks for undergraduate grade point averages of at least 3.3 to 3.5, higher for nonscience majors. Grade point averages in math and science courses, typically 3.5 or better, are particularly important. The typical midclass GRE of entering graduate students was a combined verbal and quantitative score of 1,300. Larger schools tend to place particular emphasis on math/ science grades and letters of recommendation, while smaller schools typically value a broader range of application characteristics. Students considering graduate school should make a special effort to cultivate potential letter writers, working on research projects if possible. They should also become informed about the particular requirements and values of the programs to which they are applying by visiting them if possible or by contacting professors with active research programs in the student's area of interest.

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 score0.334
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.478
Teacher spread0.385 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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