{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006708839,0.0005668487,0.0008197044,0.001501071,0.0003100345,0.00101917,0.0005593293,0.0005895167,0.00114825],"category_scores_gemma":[0.001343805,0.0003758352,0.0005634543,0.001188723,0.0002511245,0.001223328,0.0005295191,0.0003858432,0.0009089317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003095714,"about_ca_system_score_gemma":0.0005488314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008867071,"about_ca_topic_score_gemma":0.01476238,"domain_scores_codex":[0.999588,0.00005447897,0.00002130967,0.0001345149,0.0001408381,0.00006086158],"domain_scores_gemma":[0.9990953,0.0002643732,0.00008545665,0.0002036324,0.0003094286,0.00004180626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005648772,0.0002870441,0.07993418,0.0002879278,0.0002447168,0.000117743,0.0001844619,0.03309666,0.4009466,0.0006036068,0.002239843,0.4814924],"study_design_scores_gemma":[0.00003932406,0.0001040559,0.2424357,0.00003582306,0.0002907877,0.0003069975,0.0001087652,0.6492314,0.1024674,0.0009661142,0.003946947,0.0000666641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7841825,0.0007482261,0.2078042,0.0001300821,0.0001139215,0.00005151109,0.0007816293,0.003579783,0.002608051],"genre_scores_gemma":[0.8381076,0.0003739945,0.1582329,0.00008739566,0.00004155141,0.00002591082,0.001424164,0.0003378024,0.001368653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008867071,"threshold_uncertainty_score":0.01763093,"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."}}