{"id":"W2089389283","doi":"10.7490/f1000research.1423.1","title":"Segmentation and Depth from motion parallax induced dynamic occlusion","year":2011,"lang":"en","type":"article","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Open peer review; Parallax; Plant biology; Segmentation; Occlusion; Motion (physics); Computer vision; Neuroscience; Medicine; Computer science; Artificial intelligence; Biology; Internal medicine; Botany","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":[0.0003350526,0.0006057437,0.0007129938,0.001086506,0.0004323268,0.001073995,0.0007280206,0.0008315809,0.001861262],"category_scores_gemma":[0.001441397,0.0005650791,0.0004715129,0.001109197,0.0006532752,0.001193721,0.001252219,0.0008166528,0.0005048037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009078711,"about_ca_system_score_gemma":0.0008004797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004057786,"about_ca_topic_score_gemma":0.005050475,"domain_scores_codex":[0.999522,0.00004732316,0.00001320157,0.000106199,0.0002080429,0.0001032202],"domain_scores_gemma":[0.9995615,0.0001320783,0.00006465141,0.00009189318,0.0001113711,0.00003845949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001214821,0.0001148895,0.002745181,0.0003089289,0.00007658736,0.0003615365,0.0006299812,0.06259371,0.4490373,0.01692775,0.002660188,0.4633292],"study_design_scores_gemma":[0.00004467681,0.0001479551,0.007919813,0.0000268756,0.00004631559,0.0005766117,0.0001417775,0.8161811,0.1579685,0.01035284,0.006555853,0.00003771735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1597186,0.0007272432,0.8333041,0.0002506289,0.00008036352,0.00009522257,0.0003640026,0.001325958,0.004133877],"genre_scores_gemma":[0.7144915,0.0006189251,0.2785232,0.0001061582,0.00009716729,0.00006578902,0.0008119991,0.0005654125,0.004719621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004057786,"threshold_uncertainty_score":0.008068323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0673947200703024,"score_gpt":0.2745272188930201,"score_spread":0.2071324988227177,"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."}}