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Record W2765903716 · doi:10.1017/wet.2017.90

Field Pea and Lentil Tolerance to Interrow Cultivation

2017· article· en· W2765903716 on OpenAlexaffabout
Katherine Stanley, Steven J. Shirtliffe, Dilshan Benaragama, Lena D. Syrovy, Hema Duddu

Bibliographic record

VenueWeed Technology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCropAgronomyBiologyYield (engineering)WeedGrowing seasonWeed control

Abstract

fetched live from OpenAlex

Abstract Interrow cultivation is a selective, in-crop mechanical weed control tool that has the potential to control weeds later in the growing season with less crop damage compared with other in-crop mechanical weed control tools. To our knowledge, no previous research has been conducted on the tolerance of narrow-row crops to interrow cultivation. The objective of this experiment was to determine the tolerance of field pea and lentil to interrow cultivation. Replicated field experiments were conducted in Saskatchewan, Canada, in 2014 and 2015. Weekly cultivation treatments began at the 4-node stage of each crop, continuing for 6 wk. Field pea and lentil yield linearly declined with later crop stages of cultivation. Cultivating multiple times throughout the growing season reduced yield by 15% to 30% in both crops. Minimal yield loss occurred when interrow cultivation was conducted once at early growth stages of field pea and lentil; however, yield loss increased with delayed and more frequent cultivation events.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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 designObservational
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

Citations6
Published2017
Admission routes2
Has abstractyes

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