MétaCan
Menu
Back to cohort

Estimating Nitrogen Harvest Index (NHI) in field crop trials using a LECO analyzer v1

2025· article· W7128486728 on OpenAlexfundno aff
Salvador Osuna‐Caballero, Emily Pearce, Ronald A Caicedo-Garcia, Derek Wright, Kate Congreves, Curtis Pozniak, Kirstin E. Bett

Bibliographic record

Venuenot available
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
FundersGenome PrairieSaskatchewan Pulse GrowersMinistry of Agriculture - SaskatchewanGenome Canada
KeywordsSpectrum analyzerStrawNitrogenCropGrain yieldIndex (typography)Sampling (signal processing)Yield (engineering)

Abstract

fetched live from OpenAlex

Nitrogen harvest index (NHI) is a ratio of N accumulated in grain to N accumulated in grain plus straw. NHI is an important index in determining crop yield because it is positively associated with grain yield. Furthermore, it is a useful metric for quantifying variability in N allocation in plant populations, offering the possibility of breeding crops to improve the protein content of grain or straw. This research protocol is for the estimation of NHI in lentil by measuring the nitrogen content in seeds and straw from an alpha lattice design experiment using a LECO analyzer. A LECO analyzer combusts samples at high temperatures (~ ) in an oxygen-rich environment, converting nitrogen to N₂ gas, which is measured to determine N concentration. The procedure encompasses six steps from field sampling to the final NHI calculation for each experimental unit.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.300
Teacher spread0.242 · 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 designBench or experimental
Domainnot available
GenreMethods

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 topicGenetic and Environmental Crop StudiesFrench-language works237,207