{"id":"W6959486335","doi":"10.1139/geomat-2021-0008","title":"Mapping Crop Fractional Green Canopy Cover Using High Spatial Resolution Thermal and Optical Remote Sensing in Southern Ontario, Canada","year":2021,"lang":"","type":"other","venue":"TSpace","topic":"Legal and Regulatory Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Image resolution; Canopy; RGB color model; Precision agriculture; Vegetation (pathology); Spatial variability; Radiometry; Thermal","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002710789,0.0004037237,0.0002578903,0.001170462,0.001920641,0.0009196019,0.0005946267,0.0002162797,0.001003387],"category_scores_gemma":[0.0006303579,0.0002663816,0.0002648588,0.002471479,0.0005088588,0.0002824897,0.0003637416,0.0002559957,0.000188572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02091447,"about_ca_system_score_gemma":0.01904207,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971993,"about_ca_topic_score_gemma":0.9990849,"domain_scores_codex":[0.9996887,0.00001275286,0.00000968226,0.00007208477,0.0001364044,0.00008037022],"domain_scores_gemma":[0.9993045,0.00003967614,0.0000621885,0.00001936408,0.0004913858,0.0000827834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003864195,0.0001522188,0.8646594,0.0004119014,0.0002317022,0.0008517155,0.004229033,0.007427867,0.03213444,0.0006899652,0.006835388,0.08199003],"study_design_scores_gemma":[0.00001426303,0.00001540038,0.9876826,0.00003956353,0.00003193687,0.00004648126,0.001660434,0.005419808,0.0006768665,0.00004477978,0.004343657,0.0000241458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867517,0.0009197899,0.001206296,0.0002409746,0.00001200355,0.00009440662,0.003260365,0.00007434828,0.007440078],"genre_scores_gemma":[0.9907137,0.0006390964,0.002469364,0.00005977899,0.000004935286,0.00003385908,0.001840369,0.00001833041,0.0042205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02091447,"threshold_uncertainty_score":0.1517458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965036760137052,"score_gpt":0.2645620470697433,"score_spread":0.2449116794683728,"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."}}