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Record W6930601980 · doi:10.5281/zenodo.15258633

Model information and output for "The radiative effects of water vapour from terrestrial evapotranspiration"

2025· dataset· en· W6930601980 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRadiative transferReplicateSeries (stratigraphy)Water vaporScripting languageSimulation modelingCode (set theory)Source code

Abstract

fetched live from OpenAlex

This repository contains the analysis and model configuration scripts needed to replicate the simulations from "The radiative effects of water vapour from terrestrialevapotranspiration" by M. M. LagueG. R. Quetin andK. B. Heyblom The following are the model configuration files for each simulation from the study: port_cam5_iCESM_oceanQ.tarport_cam5_iCESM_fullQ.tarport_cam5_iCESM_landQ.tarlnd-trcr-f19-1850-fsst_cam5_1node.tar The following contains the modified model source code of the iCESM model that contains the necessary information to tag land-sourced water vapour: iCESM1.2_derecho.tar The following .tar files contain a subset of the compressed model output of the PORT simulations (containing radiative fluxes) presented in this study: port_cam5_iCESM_oceanQ_ts.tarport_cam5_iCESM_landQ_ts.tarport_cam5_iCESM_fullQ_ts.tar A separate repository contains the time series of the tracer output simulation (see data availability statement in the manuscript).

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.088
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0880.103

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 head0.027
GPT teacher head0.259
Teacher spread0.232 · 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 designSimulation or modeling
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicMycobacterium research and diagnosis→French-language works237,207→