{"id":"W4403243243","doi":"10.34133/plantphenomics.0268","title":"Counting Canola: Toward Generalizable Aerial Plant Detection Models","year":2024,"lang":"en","type":"article","venue":"Plant Phenomics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Canola; Field (mathematics); Artificial intelligence; Set (abstract data type); Computer science; Machine learning; Noise (video); Population; Generalizability theory; Statistics; Mathematics; Image (mathematics); Biology; Agronomy","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":[0.001372154,0.001778406,0.001012193,0.001455394,0.0004003572,0.001393128,0.002793409,0.001498641,0.001249183],"category_scores_gemma":[0.004844122,0.0009109952,0.001310188,0.0009122021,0.0006164399,0.001649778,0.001129746,0.00225795,0.0008700308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001640199,"about_ca_system_score_gemma":0.0007975986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02361791,"about_ca_topic_score_gemma":0.02723177,"domain_scores_codex":[0.9994809,0.00008218244,0.00002432932,0.0002838829,0.0000681393,0.00006063718],"domain_scores_gemma":[0.9981778,0.00117968,0.0001565741,0.0001816237,0.0002278804,0.0000764357],"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":[0.0001912236,0.000175689,0.01160637,0.0001152932,0.0001363739,0.0001175663,0.0001231517,0.8677982,0.003865281,0.001728202,0.005312677,0.1088299],"study_design_scores_gemma":[0.000007033357,0.00001245136,0.0005612594,0.000007447994,0.000007889669,0.00001294309,0.00001172333,0.9975463,0.0003373185,0.001214456,0.0002769236,0.000004339684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.343881,0.001371646,0.6368605,0.001189654,0.000124619,0.0003065457,0.003825821,0.008504256,0.003936001],"genre_scores_gemma":[0.7770582,0.0006899112,0.205906,0.0008240258,0.0001336323,0.0003728908,0.009661643,0.000520459,0.004833199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02361791,"threshold_uncertainty_score":0.04696089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0293015466956985,"score_gpt":0.1829206317113541,"score_spread":0.1536190850156556,"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."}}