{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009100443,0.00006759376,0.00005952477,0.00004351138,0.00004054902,0.00005236362,0.0001841056,0.00004177742,0.00006838922],"category_scores_gemma":[0.000009024261,0.000053721,0.0000133885,0.00006619301,0.0000124111,0.0005033521,0.0001115646,0.00004907461,0.00002514056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002411535,"about_ca_system_score_gemma":0.000005853981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000203067,"about_ca_topic_score_gemma":0.00008221873,"domain_scores_codex":[0.9994628,0.00002582129,0.0001069054,0.0001924578,0.0001191312,0.00009291422],"domain_scores_gemma":[0.9997287,0.00001056632,0.00002976381,0.0001538371,0.00003428877,0.0000428708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001879892,0.0001366244,0.01682058,0.0000063413,0.00001647284,0.000004363034,0.002051801,1.227123e-7,0.5909305,0.04007377,0.000129601,0.349811],"study_design_scores_gemma":[0.0003405965,0.0005222343,0.2994865,0.00003864674,0.000009807944,0.000002870929,0.0001040578,0.04297601,0.5962151,0.06001475,0.000009090245,0.0002802595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4677793,0.000009882482,0.519331,0.00007808373,0.00006798502,0.00008778073,1.906242e-7,0.0001879286,0.01245778],"genre_scores_gemma":[0.8983744,0.00001111507,0.1014634,0.0001079721,0.000005323596,0.000004354672,0.000002042644,0.000002341736,0.00002908213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.430595,"threshold_uncertainty_score":0.2190679,"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."}}