{"id":"W4366990643","doi":"10.3390/s23094253","title":"Development of a Quick-Install Rapid Phenotyping System","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Precision agriculture; Multispectral image; Throughput; Agricultural engineering; Field (mathematics); Computer science; Scale (ratio); Canopy; Row; Vegetation (pathology); Environmental science; Biochemical engineering; Agriculture; Engineering; Artificial intelligence; Database; Mathematics; Geography; Cartography; Biology; Telecommunications; Ecology","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":[0.001423246,0.000850894,0.0008601221,0.001092184,0.0004273244,0.0007474426,0.001581965,0.0007011025,0.01082875],"category_scores_gemma":[0.001235061,0.0007191673,0.0005252127,0.0004615097,0.0001921196,0.0008526975,0.001143342,0.0009881636,0.007925762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003232752,"about_ca_system_score_gemma":0.0007155334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198831,"about_ca_topic_score_gemma":0.001151409,"domain_scores_codex":[0.9988927,0.00008572546,0.00006850919,0.0003912152,0.0004633776,0.00009856426],"domain_scores_gemma":[0.9990821,0.0001743136,0.00007075504,0.000186906,0.0003554431,0.0001304939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007121418,0.0002188902,0.004932286,0.0004526653,0.00009667681,0.0003507946,0.0001799732,0.005171856,0.7442894,0.001407861,0.02158498,0.2206025],"study_design_scores_gemma":[0.0002501581,0.001461205,0.02868885,0.0001307447,0.0001897982,0.001707308,0.000138088,0.124475,0.6017013,0.001820141,0.2390391,0.0003982469],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03465127,0.0002312059,0.8832355,0.0002391059,0.0003201085,0.001308168,0.00521164,0.07049813,0.004304873],"genre_scores_gemma":[0.09134745,0.0002917932,0.8866137,0.0003609918,0.0001019233,0.002117758,0.007536937,0.002202743,0.009426691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01082875,"threshold_uncertainty_score":0.0362258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439598466409962,"score_gpt":0.2041549842991213,"score_spread":0.1797589996350217,"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."}}