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On the Red to Far-Red Ratios of Light Propagated by Sand-Textured Soils

2021· article· en· W3207362261 on OpenAlexafffund
Gladimir V. G. Baranoski, Mark Iwanchyshyn, Bradley W. Kimmel, Petri M. Varsa, Spencer Van Leeuwen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBiocrusts and Microbial Ecology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVegetation (pathology)Soil waterAbiotic componentRed soilEnvironmental scienceDesertificationHematiteAridSoil scienceGeologyEarth scienceEcologyMineralogyBiology

Abstract

fetched live from OpenAlex

The expansion of landscapes formed by sand-textured soils is increasing due to aridity and desertification processes elicited by human activities and climate change. Vegetation restoration initiatives are instrumental to mitigate this trend. These initiatives involve the combined use of satellite, ground-based and in silico data in the detection and management of abiotic stress factors affecting seed germination and plant development in these regions. These photobiological phenomena, in turn, are often mediated by the red to far-red ratios of impinging light. In this paper, we examine the key differences between the red to far-red ratios of light propagated by sand-textured soils characterized by either a dominant presence of hematite or goethite (limonite), the mineral impurities largely responsible for their colors. Moreover, we also address the sensitivity of these ratios to different water distribution patterns: present in these soils' pore space or forming films around their constituent grains. By strengthening the current understanding about the interconnected effects of these abiotic factors, our findings are expected to contribute to the development of new cost-effective technologies for the monitoring (in situ and remotely) of sandy landscapes and the restoration of vegetation in these regions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.996

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)0.0050.000

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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