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Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models

2023· article· en· W4323832803 on OpenAlexaff
Bruce A. Kimball, Kelly R. Thorp, Kenneth J. Boote, Claudio Stöckle, Andrew E. Suyker, Steven R. Evett, David Bräuer, Gwen G. Coyle, Karen S. Copeland, Gary W. Marek, Paul D. Colaizzi, Marco Acutis, Seyyedmajid 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, R. F. Grant, Kaiyu Guan, Gerrit Hoogenboom, Qianjing Jiang, Soo‐Hyung Kim, Isaya Kisekka, Jon Lizaso, Sara Masia, Huimin Meng, Valentina Mereu, Mukhtar Ahmed, Alessia Perego, Bin Peng, Eckart Priesack, Zhiming Qi, Vakhtang Shelia, Richard L. Snyder, Afshin Soltani, Donatella Spano, Amit Kumar Srivastava, Aimee Thomson, Dennis Timlin, Antonio Trabucco, Heidi Webber, Tobias K. D. Weber, Magali Willaume, Karina Williams, Michael van der Laan, Domenico Ventrella, Michelle Viswanathan, Xu Xu, Wang Zhou

Bibliographic record

VenueAgricultural and Forest Meteorology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersOffice of Experimental Program to Stimulate Competitive ResearchCooperative State Research, Education, and Extension ServiceU.S. Department of AgricultureOffice of ScienceMet OfficeU.S. Department of Energy
KeywordsEvapotranspirationEnvironmental scienceEddy covarianceIrrigation schedulingIrrigationLysimeterTranspirationSimulation modelingHydrology (agriculture)BiometeorologyCrop simulation modelCrop coefficientCanopyCrop yieldAtmospheric sciencesAgronomySoil waterSoil scienceEcosystemEcologyMathematicsGeologyBiology

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 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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.225
Teacher spread0.205 · 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 teacher head, 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

Citations51
Published2023
Admission routes1
Has abstractno

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