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Record W3134542655 · doi:10.1002/agj2.20652

Changes in leaf optical properties during tomato, common lamb's‐quarters, and redroot pigweed plant development

2021· article· en· W3134542655 on OpenAlexafffund
Li Ma, Mahesh K. Upadhyaya

Bibliographic record

VenueAgronomy Journal · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversity of British ColumbiaKwantlen Polytechnic University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgronomyBiology

Abstract

fetched live from OpenAlex

Abstract Leaf optical properties influence the red/far‐red light ratio (a signal of potential inter‐plant interaction) in plant canopies, which could alter inter‐plant interactions among species. Differences in leaf optical properties of species comprising a mixed population have significant implications for plant–plant interaction. Leaf optical properties at red (660 nm) and far‐red (730 nm) lights for the third true leaf of tomato ( Solanum lycopersicon L.) and its two weeds common lamb's‐quarters ( Chenopodium album L.) and redroot pigweed ( Amaranthus retroflexus L.) were compared during plant development. The results showed that optical properties of the third true leaf at 660 and 730 nm changed with plant development in common lamb's‐quarters, redroot pigweed, and tomato, and the three species differed in this regard. Red/far‐red ratios of reflected (R ratio ) and transmitted (T ratio ) lights also changed with plant development in all species. R ratio and T ratio were greater in redroot pigweed compared to common lamb'squarters and tomato. These ratios significantly related with chlorophyll content and leaf mass per area. These differences, usually neglected, could influence growth and intra‐ and/or inter‐species interactions in plant communities comprising these species. These results provide valuable information for understanding eco‐physiology and function of vegetation cover and could help in development of effective weed management strategies.

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)0.000
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 score0.669
Threshold uncertainty score0.198

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 head0.018
GPT teacher head0.187
Teacher spread0.168 · 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 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

Citations1
Published2021
Admission routes2
Has abstractyes

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