{"id":"W2302272972","doi":"10.1016/j.agrformet.2016.03.008","title":"Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography","year":2016,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sky; Pixel; Remote sensing; Canopy; Digital image; Digital photography; Zenith; Environmental science; Fraction (chemistry); Digital camera; Diffuse sky radiation; Mathematics; Photography; Scattering; Computer science; Optics; Artificial intelligence; Image processing; Image (mathematics); Physics; Geography; Meteorology; Chemistry","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":[0.0000469392,0.0001463108,0.0001706379,0.00002545689,0.00009479145,0.00003143815,0.00006335184,0.0001175721,0.00001479626],"category_scores_gemma":[0.00003928949,0.00006580564,0.00005133623,0.0001395608,0.0001555271,0.0005108249,0.00002981703,0.00005506741,0.00001846739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004978389,"about_ca_system_score_gemma":0.000002227888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009877871,"about_ca_topic_score_gemma":0.0001739244,"domain_scores_codex":[0.9992177,0.00002261133,0.0001883839,0.0002921571,0.0001198581,0.0001593133],"domain_scores_gemma":[0.9994607,0.0001479703,0.000212776,0.00009685007,0.00003213151,0.00004957856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002361736,0.00004133239,0.02649869,0.000005267927,0.00003047415,1.818895e-7,0.00006962193,0.0001677208,0.9427862,0.00003324917,0.00142446,0.02870667],"study_design_scores_gemma":[0.0006091995,0.0005772872,0.9560724,0.00002712195,0.00004773662,0.00002158628,0.00005953663,0.002426695,0.03870961,0.0007533683,0.0005404576,0.0001550175],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913678,0.00001028796,0.006917473,0.0005754985,0.0002503202,0.000347674,0.0000145782,0.00003953511,0.0004768056],"genre_scores_gemma":[0.9986393,0.0000149608,0.0009889114,0.00003010424,0.00004825998,0.00001483997,0.0001006496,0.000006581342,0.0001563759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9295737,"threshold_uncertainty_score":0.2683476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006156135357066995,"score_gpt":0.1855232755695704,"score_spread":0.1793671402125034,"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."}}