{"id":"W2081244766","doi":"10.1007/s11540-007-9021-x","title":"Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study","year":2007,"lang":"en","type":"article","venue":"Potato Research","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Red River College","funders":"Western Economic Diversification Canada","keywords":"Greenhouse; RGB color model; Environmental science; Irrigation; Canopy; Solanum tuberosum; Digital image; Digital camera; Mathematics; Remote sensing; Agronomy; Horticulture; Image processing; Botany; Artificial intelligence; Computer science; Biology; Geography; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001789474,0.0003327572,0.00027789,0.0001577601,0.0001555756,0.0002737214,0.000360254,0.000389763,0.0006096878],"category_scores_gemma":[0.0005061786,0.0002017043,0.0003290623,0.0002906041,0.0002068636,0.000465377,0.000112972,0.0002822807,0.00008174009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005587933,"about_ca_system_score_gemma":0.0002342361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02447414,"about_ca_topic_score_gemma":0.01368865,"domain_scores_codex":[0.9999566,0.00001175372,0.000001935798,0.00001592928,0.000008863318,0.000004926673],"domain_scores_gemma":[0.99966,0.0002489405,0.00001907997,0.00001625224,0.00004724716,0.000008538185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001982412,0.0001156694,0.003862021,0.00005908596,0.00003160717,0.00007457272,0.00005540844,0.9576982,0.02665889,0.0003043901,0.0002042113,0.01073763],"study_design_scores_gemma":[0.000004905478,0.00003033171,0.001952005,0.000001155202,0.000005861807,0.000006417113,0.00001088406,0.99496,0.002903746,0.00007618355,0.00004390661,0.000004614686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987748,0.00007756235,0.01094253,0.00003114674,0.000006769339,0.000008438713,0.00007945289,0.00007581954,0.001030304],"genre_scores_gemma":[0.9974261,0.00004297487,0.002020883,0.000003331526,0.00000106659,0.000009581906,0.00005289403,0.000008195479,0.0004349055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02447414,"threshold_uncertainty_score":0.04866338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08899791302246898,"score_gpt":0.3312581936947491,"score_spread":0.2422602806722801,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}