MétaCan
Menu
Back to cohort

Crop metrics for nutrient management research in corn-based cropping systems v1

2025· preprint· en· W4410110772 on OpenAlexaboutno aff
Lori Abendroth, Tai McClellan Maaz, Sylvie M. Brouder, Cynthia A. Cambardella, Giorgi Chighladze, Laura E. Christianson, C. F. Drury, Fabián G. Fernández, Lowell E. Gentry, Matthew J. Helmers, D. B. Jaynes, John L. Kovar, Thomas B. Moorman, Kelly A. Nelson, Cameron M. Pittelkow, John E. Sawyer, Jeffrey J. Volenec, Alison J. Eagle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
FundersInternational Plant Nutrition Institute
KeywordsCroppingNutrient managementAgronomyCropCropping systemCrop managementAgroforestryZea maysAgricultural engineeringNutrientEnvironmental scienceBiologyAgricultureEngineeringEcology

Abstract

fetched live from OpenAlex

This protocol was used by a collaborative research team referred to as the “NutriNet Project” in corn-based cropping systems of the U.S. Midwest and Canada. Plant measurements were collected from field-based experiments to determine aboveground biomass and nutrient concentration at different crop development stages. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) are the cash crops and cereal rye (Secale cereale L.) is the cover crop described in this protocol. The total amount of nutrient uptake is calculated based on the plant biomass and nutrient concentration measurements. Nutrient analysis was conducted using dried, ground plant samples, separated into grain and non-grain components when applicable. Ancillary measurements, such as chlorophyll meter readings and corn stalk nitrate concentrations, are also described as these provide important context for informing nutrient management decisions. Corn was the dominant crop and focus of the project team when collecting measurements; this is evident by the detail describing corn in this protocol.

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 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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.188
GPT teacher head0.362
Teacher spread0.174 · 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 designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicCrop Yield and Soil FertilityFrench-language works237,207