{"id":"W4380434529","doi":"10.1016/bs.agron.2023.05.004","title":"Remote and proximal sensing: How far has it come to help plant breeders?","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in agronomy","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"","keywords":"Pace; Agriculture; Plant breeding; Population; Biotechnology; Geography; Remote sensing; Environmental resource management; Biology; Agronomy; Ecology; Environmental science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001283873,0.0003845003,0.0004553683,0.00003777181,0.0001803151,0.0001661864,0.0002306905,0.0002503408,0.00007834977],"category_scores_gemma":[0.00002010568,0.0001523663,0.0001026649,0.000112843,0.0001259092,0.0002240537,0.0001924295,0.0003017051,0.0001477383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004453901,"about_ca_system_score_gemma":0.00001025681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000929256,"about_ca_topic_score_gemma":0.007097216,"domain_scores_codex":[0.9984707,0.0000188897,0.0002532664,0.0006453745,0.0002225426,0.0003892548],"domain_scores_gemma":[0.9993699,0.0002089262,0.000135956,0.00008437362,0.00004414022,0.0001567635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001527763,0.00004152828,0.001355407,0.0001219179,0.0001011113,0.0003701975,0.0005163247,0.00002581125,0.002154417,0.01464076,0.06186699,0.9186528],"study_design_scores_gemma":[0.0001113896,0.0001802296,0.002336138,0.0003003187,0.0000158152,0.00002772727,0.000251225,0.00001141238,0.00005840371,0.003081902,0.9931473,0.0004781816],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.03827908,0.02113798,0.0001251144,0.1515169,0.003480987,0.006033455,0.001221115,0.001119326,0.7770861],"genre_scores_gemma":[0.07147241,0.007278878,0.00353276,0.004896257,0.004286564,0.0000328116,0.001724944,0.00002986486,0.9067455],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9312803,"threshold_uncertainty_score":0.6213318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03386531386294615,"score_gpt":0.2189486124554174,"score_spread":0.1850832985924712,"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."}}