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Record W2738901854 · doi:10.1139/cjps-2016-0376

CHEMOTYPING USING SYNCHROTRON MID-INFRARED AND X-RAY SPECTROSCOPY TO IMPROVE AGRICULTURAL PRODUCTION

2017· article· en· W2738901854 on OpenAlexafffundvenueabout
Karen Tanino, Ian R. Willick, Kaila Hamilton, Perumal Vijayan, Yunfei Jiang, Gurcharn S. Brar, Peiqiang Yu, Lee Kalcsits, Rachid Lahlali, B.A. Smith, Brian Fowler, H. Randal Kutcher, Rosalind Bueckert, Chithra Karunakaran

Bibliographic record

VenueCanadian Journal of Plant Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsSynchrotronEnvironmental scienceMaterials scienceComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Synchrotron techniques are powerful tools in material and environmental sciences; however, they are currently underutilized in plant research. The Canadian Light Source synchrotron at the University of Saskatchewan campus is the only such facility in Canada open to academic, government, and industrial clients. This review introduces the potential of synchrotron-based spectroscopic methods and its applications to agriculture and plant sciences. Relative ease of sample preparation, nondestructive analysis, high spatial resolution, and multiple response measurements within a single sample are among its advantages. Synchrotron-based Fourier transform mid-infrared spectromicroscopy, X-ray absorption, and fluorescence spectromicroscopy are included in the several approaches discussed. Examples range from evaluating protein secondary structure and nondestructive compositional analysis of leaf epicuticular wax and pollen surface lipids to cell wall composition and nutrient analyses. Synchrotron technology can help to initially identify key spectra related to plant properties for subsequent higher throughput techniques. One example is the adaptation of synchrotron techniques for lower resolution analysis in the field such as nondestructive elemental analysis for localization of nutrients in fruit crops using handheld high-throughput devices. In addition, interest in creating high-throughput systems based on synchrotron technology itself is driving the development of new hardware to meet these larger challenges.

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 categoriesScience and technology studies
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 score0.930
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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

Citations12
Published2017
Admission routes4
Has abstractyes

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