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Record W2005177390 · doi:10.1080/10798587.2015.1015774

Intelligent Information Technologies in Fruit Industry

2015· article· en· W2005177390 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIntelligent Automation & Soft Computing · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBeijingChinaChinese academy of sciencesEliteAgricultureLibrary scienceChinese societyManagementPolitical scienceComputer scienceGeographyLaw

Abstract

fetched live from OpenAlex

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsLie DengL. Deng received B.Sc. degree in Pomology from Southwest Agricultural College, China in 1982. His research interests include intelligent systems, citrus physiology and culture technology, systems modeling and analysis. Professor Deng is the Elite researcher of the Chinese Academy of Agricultural Sciences, standing committee member of the Chinese Society of Citriculture, Vice-president of the Citrus Research Institute under the Southwest University and the deputy director of the Chongqing Society of Horticulture, and Editor-in-chief of the China Fruit News.Qiang LyuQ. Lyu received B.Sc. degree in Food Sciences from Henan Institute of Technology, Xinxiang, China in 2002, M.Sc. degree in Food Sciences from Jiangsu University, Zhenjiang, China in 2008, and Ph.D. degree in Food Sciences from the Jiangsu University, Zhenjiang, China in 2010. Associate Professor Lyu joined the Citrus Research Institute of Chinese Academy of Agricultural Sciences in 2011. His research interests include remote sensing for precision application, harvesting robot, and image processing.Simon X. YangS. X. Yang received B.Sc. degree in Engineering Physics from Beijing University, China in 1987, the first of two M.Sc. degrees in Biophysics from Chinese Academy of Sciences, Beijing, China in 1990, the second M.Sc. degree in Electrical Engineering from the University of Houston, USA in 1996, and the Ph.D. degree in Electrical and Computer Engineering from the University of Alberta, Edmonton, Canada in 1999. Professor Yang joined the School of Engineering at the University of Guelph, Canada in 1999. Currently he is a Professor and the Head of the Advanced Robotics and Intelligent Systems (ARIS) Laboratory at the University of Guelph in Canada. His research interests include intelligent systems, robotics, sensors and multi-sensor fusion, wireless sensor networks, control systems, soft computing, systems modeling and analysis, and computational neuroscience. Professor Yang serves as the Editor-in-Chief of Journal of Robotics and Artificial Intelligence and International Journal of Complex Systems – Computing, Sensing and Control, and an Associate Editor of IEEE Transactions on Cybernetics and several other journals. He has involved in the organization of many conferences. He is the 2011 IEEE International Conference on Logistics and Automation.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.249
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