MétaCan
Menu
Back to cohort
Record W4389664789 · doi:10.1038/s43247-023-01145-1

Author Correction: Global survey shows planners use widely varying sea-level rise projections for coastal adaptation

2023· article· en· W4389664789 on OpenAlexaff
Daniella Hirschfeld, David Béhar, Robert J. Nicholls, Niamh Cahill, T. S. James, Benjamin P. Horton, Michelle Ε. Portman, Robert G. Bell, Matthew Campo, Miguel Esteban, Bronwyn Goble, Munsur Rahman, Kwasi Appeaning Addo, Faiz Ahmed Chundeli, Monique Aunger, Orly Babitsky, Anders Beal, Ray Boyle, Jiayi Fang, Amir Gohar, Susan Hanson, Saul Karamesines, Myungjin Kim, Hilary Lohmann, Kathleen L. McInnes, Nobuo Mimura, Doug Ramsay, Landis Wenger, Hiromune YOKOKI

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsAdaptation (eye)Sea level riseEnvironmental scienceGeographyComputer scienceClimatologyGeodesyEnvironmental resource managementOceanographyMeteorologyGeologyClimate changePsychology

Abstract

fetched live from OpenAlex

Correction to: Communications Earth & Environment https://doi.org/10.1038/s43247-023-00703-x , published online 03 April 2023

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

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

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 head0.140
GPT teacher head0.282
Teacher spread0.141 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCommunications Earth & EnvironmentSame topicCoastal and Marine DynamicsFrench-language works237,207