MétaCan
Menu
Back to cohort
Record W2115203545 · doi:10.1029/2007eo500006

Attracting, retaining, and engaging early career scientists

2007· article· en· W2115203545 on OpenAlexaboutno aff
Alan Jones, Kate V. Heal, Daniel Pringle

Bibliographic record

VenueEos · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Political scienceLibrary sciencePublic administrationComputer science

Abstract

fetched live from OpenAlex

This young scientists event was organized to engage younger scientists with the International Union of Geodesy and Geophysics (IUGG) and to provide a specific forum to express their views at the General Assembly. It comprised a panel discussion chaired by Kate Heal and with three young geosciences panelists (Masaki Hayashi, University of Calgary, Canada; Kalachand Sain, National Geophysical Research Institute, Hyderabad, India; and Simona Stefanescu, National Meteorological Administration, Bucharest). The group, which had identified several topics relevant to young geoscientists, presented their views in open discussion session. Thirty IUGG conference attendees were present.

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)0.020
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.980
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.002
Scholarly communication0.0180.004
Open science0.0020.017
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0180.012

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 head0.028
GPT teacher head0.277
Teacher spread0.249 · 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.

Study designNot applicable
DomainIncentives
GenreCommentary

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 venueEosSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207