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Record W2996192334 · doi:10.1139/cjps-2019-0106

Growth and vegetable yield of <i>Amaranthus hybridus</i> L. as impacted by harvest methods in southern Ontario, Canada

2019· article· en· W2996192334 on OpenAlexaffvenueabout
Geoff Farintosh, Rachel Riddle, Rene Van Acker

Bibliographic record

VenueCanadian Journal of Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAmaranthAmaranthus hybridusCropYield (engineering)Leafy vegetablesBiologyAgronomyHorticultureWeed

Abstract

fetched live from OpenAlex

Amaranthus hybridus L. is a nutritious leafy vegetable amaranth species grown primarily in tropical regions. Weedy amaranth species thriving in southern Ontario suggest the edible biotype could also grow efficiently as a crop. Field trials in Guelph, Simcoe, and Stouffville, Ontario, examined yield in response to three harvest frequencies (weekly, every 2 wk, and every 3 wk) cut at 15 cm above ground level, using two traditional harvest practices (Afro-Caribbean and European) cut every 2 wk, and a control which was not cut until the final harvest. The highest marketable yields and quality measures were observed in plants cut every 2 wk, every 3 wk, and using the Afro-Caribbean cutting technique which selects and harvests thicker stems while leaving new shoots for later harvests. The difference in growth among sites suggests plants prefer warm, well-drained soil. Marketable yield was as high as 4.02 ± 0.340 kg m −2 in Simcoe with an average yield across all three sites of 2.37 ± 0.229 kg m −2 . This demonstrates that A. hybridus has the potential to be grown as a vegetable crop in southern Ontario and that marketable yield can be optimized by method of harvest.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.189
Teacher spread0.180 · 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 designBench or experimental
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
Published2019
Admission routes3
Has abstractyes

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