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Record W4402324250 · doi:10.2139/ssrn.4948334

Evaluation of Multimodel Averaging Approaches for Ensembling Evapotranspiration and Yield Simulations from Maize Models

2024· preprint· en· W4402324250 on OpenAlexaff
Viveka Nand, Zhiming Qi, Liwang Ma, Matthew J. Helmers, Chandra A. Madramootoo, Ward Smith, T.Q. Zhang, Tobias K. D. Weber, Elizabeth Pattey, Ziwei Li, Jiaxin Wang, Virginia L. Jin, Qianjing Jiang, Mario Tenuta, Thomas J. Trout, Miao Hao Cheng, R. Daren Harmel, Bruce A. Kimball, Kelly R. Thorp, Kenneth J. Boote, Claudio O. Stöckle, Andrew E. Suyker, Steven R. Evett, David Bräuer, Gwen G. Coyle, Karen S. Copeland, Gary Merek, Paul D. Colaizzi, Marco Acutis, Seyyed Majid Alimagham, Sotirios V. Archontoulis, Babacar Faye, Zoltán Barcza, Bruno Basso, Patrick Bertuzzi, Julie Constantin, Massimiliano De Antoni Migliorati, Benjamin Dumont, J. L. Durand, Nándor Fodor, Thomas Gaiser, Pasquale Garofalo, Sebastian Gayler, Luisa Giglio, Robert R. Grant, Kaiyu Guan, Gerrit Hoogenboom, Soo‐Hyung Kim, Isaya Kisekka, Jon Lizaso, Sara Masia, Huimin Meng, Valentina Mereu, Mukhtar Ahmed, Alessia Perego, Bin Peng, Eckart Priesack, Vakhtang Shelia, Richard L. Snyder, Afshin Soltani, Donatella Spano, Amit K. Srivastava, Aimee Thomson, Dennis Timlin, Antonio Trabucco, Heidi Webber, Magali Willaume, Karina Williams, Michael van der Laan, Domenico Ventrella, Michelle Viswanathan, Xu Xu, Wang Zhou

Bibliographic record

VenueSSRN Electronic Journal · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of ManitobaGovernment of CanadaAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsEvapotranspirationYield (engineering)Environmental scienceComputer scienceEcologyBiologyPhysics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.006
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.266
Teacher spread0.189 · 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
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

Citations0
Published2024
Admission routes1
Has abstractno

Explore more

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