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Record W4387141343 · doi:10.1088/1748-9326/acfb97

Corrigendum: Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs (2023 Environ. Res. Lett. 18 034046)

2023· erratum· en· W4387141343 on OpenAlexaff
Małgorzata Gołub, Nikaan Koupaei‐Abyazani, Timo Vesala, Ivan Mammarella, Anne Ojala, Gil Bohrer, Gesa A. Weyhenmeyer, Peter D. Blanken, Werner Eugster, Franziska Koebsch, Jiquan Chen, Kevin Czajkowski, Chandrashekhar Deshmukh, Frédéric Guérin, Jouni Heiskanen, Elyn Humphreys, Anders Jönsson, Jan Karlsson, George W. Kling, Xuhui Lee, Heping Liu, Annalea Lohila, Erik Lundin, Tim Morin, Eva Podgrajsek, Maria Provenzale, Anna Rutgersson, Torsten Sachs, Erik Sahlée, D. Serça, Changliang Shao, Christopher Spence, Ian B. Strachan, Wei Xiao, Ankur R. Desai

Bibliographic record

VenueEnvironmental Research Letters · 2023
Typeerratum
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsQueen's UniversityEnvironment and Climate Change CanadaCarleton University
FundersDivision of Environmental BiologyHORIZON EUROPE Framework ProgrammeNational Oceanic and Atmospheric AdministrationBeijing Normal UniversityAcademy of FinlandOffice of ScienceHelsingin YliopistoLeibniz-GemeinschaftElectricité de FranceU.S. Department of EnergyLeibniz-Institut für Gewässerökologie und BinnenfischereiVetenskapsrådetNational Science FoundationOhio Department of Natural Resources
KeywordsDiel vertical migrationVariation (astronomy)Carbon dioxideSentenceEnvironmental scienceOceanographyEcologyComputer scienceGeologyBiologyPhysicsArtificial intelligenceAstrophysics

Abstract

fetched live from OpenAlex

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not.The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

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)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.1150.068

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 head0.020
GPT teacher head0.253
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.

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

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