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Record W3045777412 · doi:10.1038/s41597-020-00590-3

Author Correction: Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater

2020· erratum· en· W3045777412 on OpenAlex
Tunde Olarinoye, Tom Gleeson, Vera Marx, Stefan Seeger, Rouhollah Adinehvand, Vincenzo Allocca, Bartolomé Andreo, James Apaéstegui, Christophe Apolit, Bruno Arfib, Augusto S. Auler, Vincent Bailly-Comte, Juan Antonio Barberá, Christelle Batiot‐Guilhe, Timothy Bechtel, Stéphane Binet, Daniel Bittner, Matej Blatnik, Terry Bolger, Pascal Brunet, Jean‐Baptiste Charlier, Zhao Chen, Gabriele Chiogna, Gemma Coxon, Pantaleone De Vita, Joanna Doummar, Jannis Epting, Perrine Fleury, Matthieu Fournier, Nico Goldscheider, John Gunn, Fang Guo, Jean‐Loup Guyot, Nicholas Howden, Peter Huggenberger, Brian B. Hunt, Pierre‐Yves Jeannin, Guanghui Jiang, Greg Jones, Hervé Jourde, Ivo Karmann, Oliver Koit, Jannes Kordilla, David Labat, Bernard Ladouche, Isabella Serena Liso, Zaihua Liu, Jean‐Christophe Maréchal, Nicolas Masséi, Naomi Mazzilli, Matías Mudarra, Mario Parise, Junbing Pu, Nataša Ravbar, Liz Hidalgo Sanchez, Antonio Santo, Martin Sauter, Jean‐Luc Seidel, Vianney Sivelle, Rannveig Øvrevik Skoglund, Zoran Stevanović, Cameron Wood, Stephen R. H. Worthington, Andreas Hartmann

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Data · 2020
Typeerratum
Languageen
FieldEarth and Planetary Sciences
TopicKarst Systems and Hydrogeology
Canadian institutionsUniversity of Victoria
FundersNatural Environment Research CouncilSight Research UK
KeywordsKarstHydrographGroundwaterHydrology (agriculture)GeologyWater resource managementEnvironmental scienceGeographyPaleontologyArchaeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.132
GPT teacher head0.346
Teacher spread0.215 · 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