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Record W4401011548 · doi:10.1038/s41612-024-00719-w

Author Correction: Can climate change signals be detected from the terrestrial water storage at daily timescale?

2024· article· en· W4401011548 on OpenAlexaff
Fei Huo, Li Xu, Zhenhua Li, Yanping Li, J. S. Famiglietti, Hrishi A. Chandanpurkar

Bibliographic record

Venuenpj Climate and Atmospheric Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsWestern University
Fundersnot available
KeywordsClimate changeEnvironmental scienceWater storageClimatologyAtmospheric sciencesMeteorologyGeologyGeographyOceanography

Abstract

fetched live from OpenAlex

Correction to: npj Climate and Atmospheric Science https://doi.org/10.1038/s41612-024-00646-w , published online 2 July 2024

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)0.004
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.165
Threshold uncertainty score0.551

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.079
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.1650.090

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 head0.035
GPT teacher head0.239
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

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