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Record W1897941944 · doi:10.21273/horttech.16.3.0408

Adapting Nitrogen Fertilization to Unpredictable Seasonal Conditions with the Least Impact on the Environment

2006· article· en· W1897941944 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHortTechnology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsEnvironmental scienceContext (archaeology)Growing seasonCrop managementAgricultural engineeringHuman fertilizationCrop insuranceCropAgronomyEnvironmental resource managementAgricultureGeographyEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Weather is the primary driver of both plant growth and soil conditions. As a consequence of unpredictable weather effects on crop requirements, more inputs are being applied as an insurance policy. Best management practices (BMPs) are therefore about using minimal input for maximal return in a context of unpredictable weather events. This paper proposes a set of complementary actions and tools as BMP for nitrogen (N) fertilization of vegetable crops: 1) planning from an N budget, 2) reference plot establishment, and 3) crop sensing prior to in-season N application based on a saturation index related to N requirement.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.442

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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