{"id":"W4405968971","doi":"10.3390/plants14010088","title":"A Feasibility Study on Utilizing Remote Sensing Data to Monitor Grape Yield and Berry Composition for Selective Harvesting","year":2024,"lang":"en","type":"article","venue":"Plants","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Berry; Vineyard; Winemaking; Remote sensing; Multispectral image; Vegetation (pathology); Yield (engineering); Environmental science; Terroir; Geography; Horticulture; Wine; Medicine; Food science; Biology","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":[0.003204467,0.0003851723,0.0001723256,0.0005995479,0.0003908609,0.0005316072,0.0003919842,0.0003244616,0.001061352],"category_scores_gemma":[0.002942905,0.0002002902,0.0002773098,0.000589411,0.000185027,0.001070794,0.0003965763,0.0001918507,0.0002659162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003237901,"about_ca_system_score_gemma":0.001016155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004371618,"about_ca_topic_score_gemma":0.008589033,"domain_scores_codex":[0.9988499,0.0005909098,0.00005507409,0.0001611086,0.0002744937,0.00006850468],"domain_scores_gemma":[0.9981066,0.0007465707,0.0002349628,0.0001632826,0.0006203146,0.0001282611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002414316,0.002843198,0.6062245,0.0004871032,0.0001069519,0.0009749215,0.001140249,0.003868593,0.1835569,0.0009613,0.0006063045,0.1968155],"study_design_scores_gemma":[0.0002526585,0.01936498,0.8917305,0.00008287027,0.0001816481,0.0008968599,0.003772317,0.03004393,0.0474374,0.000761272,0.005394547,0.00008104119],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796534,0.0001030246,0.01558545,0.0001751675,0.00001615968,0.0008472833,0.0002732975,0.00005261237,0.003293645],"genre_scores_gemma":[0.9707661,0.0001176792,0.0278534,0.00004537703,0.00001268309,0.0002731282,0.0002420458,0.000006117264,0.0006835225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004371618,"threshold_uncertainty_score":0.01694703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.280863975350424,"score_gpt":0.3872564629194714,"score_spread":0.1063924875690473,"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."}}