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Record W4281550724 · doi:10.1139/cjps-2022-0002

Ivyleaf morningglory (<i>Ipomoea hederacea</i> Jacq.) competition is not intensified by drought in silage corn in central New York State, USA

2022· article· en· W4281550724 on OpenAlexvenueno aff
Kristine M. Averill, Scott H. Morris, Anna S. Westbrook, Antonio DiTommaso

Bibliographic record

VenueCanadian Journal of Plant Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsSilageAgronomyBiologySowingCropDrought tolerance

Abstract

fetched live from OpenAlex

Ivyleaf morningglory (IMG, Ipomoea hederacea Jacq.) is a summer annual vine that is native to the Americas and invasive globally. This species decreases field crop yields through competition and interference with harvesting. Here, we explore the potential of IMG to compete with silage corn ( Zea mays L.) in New York State, USA. In a 2-year field study, we measured silage yield at five IMG planting densities (0–8 plants m −2 ) under no-drought conditions and a drought treatment established with rainout shelters. Volumetric water content was 26%–28% lower in the drought treatment than the no-drought treatment. Drought reduced fresh corn silage yield ( P = 0.003). Fresh silage yield was 21 600 ± 700 kg ha −1 in the no-drought treatment in 2016, 19 100 ± 900 kg ha −1 in the drought treatment in 2016, 30 500 ± 900 kg ha −1 in the no-drought treatment in 2017, and 28 200 ± 700 kg ha −1 in the drought treatment in 2017. Silage yield was not strongly responsive to IMG density, regardless of drought. These data suggest that the risk of corn silage yield losses due to IMG is relatively low in New York State and unlikely to be affected by drought. The risk posed by IMG may differ in other cropping systems or regions and under climate change.

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 categoriesInsufficient payload (model declined to judge)
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 score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.230
Teacher spread0.194 · 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.

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
Published2022
Admission routes1
Has abstractyes

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