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Record W2981672697 · doi:10.1117/12.2532495

Porosity effects on red to far-red ratios of light transmitted in natural sands: implications for photoblastic seed germination

2019· article· en· W2981672697 on OpenAlexaff
Gladimir V. G. Baranoski, Bradley W. Kimmel, Petri M. Varsa, Mark Iwanchyshyn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFar-redGerminationPorosityNatural (archaeology)Environmental scienceBotanyRed lightGeologyBiologyGeotechnical engineeringPaleontology

Abstract

fetched live from OpenAlex

Seed germination corresponds to the first and crucial stage of a plant’s life cycle. It is directly affected by water availability and soil characteristics, notably porosity. The seeds of many plant species are known to be photoblastic, i.e., their germination is also significantly affected by light exposure. A comprehensive understanding about the interconnected effects of these abiotic factors on seed germination is essential for the success of a broad range of applied research initiatives in agriculture and ecology. These initiatives include, for example, studies involving the germination of stress-adapted seeds in arid regions, like perennial desert habitats and desertified landscapes, and the germination of weed seeds in arable fields that may be covered by sand-textured soils (commonly referred to as natural sands). The germination of photoblastic seeds depends not only on the amount, but also on the spectral quality of the impinging light. This radiometric parameter can be expressed in terms of the ratio between red and far-red light reaching these seeds. In this research, we unveil the impact of variations in the porosity of sand-textured soils on their red to far-red ratios of transmitted light. Although one may expect that porosity can affect these ratios and, consequently, the germination of photoblastic seeds in natural sands, no systematic study about these putative connections has been reported in the literature to date. To some extent, this can be attributed to testing limitations posed by the actual handling of these granular materials, such as grain breakage and pore space disturbance, during investigations based on traditional experimental procedures. Moreover, the scant available information on these connections has been mostly derived from analyses performed on laboratory-prepared samples, which often present morphological characteristics that conspicuously differ from those of naturally-occurring deposits of these soils. In order to overcome these constraints, we employ an in silico investigation framework to carry out controlled light transmission experiments considering realistic characterizations of dry and water-saturated samples of natural sands. This framework is supported by measured spectral data and the use of a first-principles light transport model that explicitly accounts for the particulate structure of these materials. Our in silico experimental results provide a comprehensive depiction of the changes in the red to far-red ratios of light transmitted through natural sand layers of variable thickness due to variations on their porosity. Moreover, they also show that these changes are markedly modulated by the presence of water in these layers. Thus, our findings establish a predictive relationship between porosity and the light-elicited germination of photoblastic seeds in sand-textured soils subject to distinct degrees of water saturation. Accordingly, they are expected to contribute to the development of innovative technologies aimed at the predictive assessment (in situ or remote) of the compound impact of these abiotic factors on seed germination. These technologies, in turn, are likely to lead to new costeffective solutions for ongoing challenges in agriculture (e.g., crop yield enhancement) and ecology (e.g., invasive plant detection and vegetation restoration), particularly with respect to regions susceptible to extreme environmental conditions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.204

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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