MétaCan
Menu
Back to cohort
Record W4392706000 · doi:10.1175/mwr-1523masthead

Masthead

2024· paratext· en· W4392706000 on OpenAlexfundno aff

Bibliographic record

VenueMonthly Weather Review · 2024
Typeparatext
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
FundersPacific Northwest National LaboratorySandia National LaboratoriesU.S. Naval Research LaboratoryOffice of Naval ResearchUniversity at AlbanyUniversity of Colorado BoulderMarshall Space Flight CenterJet Propulsion LaboratoryEidgenössische Technische Hochschule ZürichNational Oceanic and Atmospheric AdministrationStony Brook UniversitySun Yat-sen UniversityEnvironment and Climate Change CanadaNational Central UniversityCleveland State UniversityDanmarks Tekniske UniversitetNanjing UniversityUniversity of Wisconsin-MadisonUniversità degli Studi di TrentoMississippi State UniversityNorth Carolina State UniversityTexas Tech UniversityChina Meteorological AdministrationUniversity of OklahomaNational Aeronautics and Space AdministrationUniversity of MiamiGoddard Space Flight CenterDeutsches Zentrum für Luft- und RaumfahrtPennsylvania State University
KeywordsMeteorologyGeologyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

MONTHLY WEATHER REVIEW (MWR) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies.This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments.MWR also addresses phenomena having seasonal and subseasonal time scales.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: Other
Teacher disagreement score0.150
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8500.809

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 head0.039
GPT teacher head0.273
Teacher spread0.233 · 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
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Weather ReviewSame topicMeteorological Phenomena and SimulationsFrench-language works237,207