{"id":"W4386698816","doi":"10.3389/fcomp.2023.1140723","title":"The mid-level vision toolbox for computing structural properties of real-world images","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computer Science","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Computer science; Artificial intelligence; Curvature; Computer vision; Toolbox; Polygon (computer graphics); Visualization; Contour line; Pattern recognition (psychology); Geometry; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001565001,0.0001359287,0.0001896086,0.0005075353,0.0007276314,0.0004146574,0.001755469,0.00002890644,2.54722e-7],"category_scores_gemma":[0.00006445058,0.00009397797,0.00007957865,0.002791504,0.000503173,0.0008441133,0.0004873732,0.0001066122,0.000003424501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009131376,"about_ca_system_score_gemma":0.0001224888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004493002,"about_ca_topic_score_gemma":0.00002407315,"domain_scores_codex":[0.9979854,0.00007013727,0.0004040121,0.0005256633,0.0005325843,0.0004821945],"domain_scores_gemma":[0.9990018,0.00008648563,0.000159537,0.0004684412,0.000218451,0.0000652773],"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":[0.000034615,0.00003460011,0.00834484,0.00007994839,0.00001218201,0.000003735698,0.001911711,0.008332975,0.01448043,0.006119716,0.006016142,0.9546291],"study_design_scores_gemma":[0.0002530633,0.0001056246,0.05992337,0.00006516523,0.000001421221,0.000002561059,0.00005270102,0.9182494,0.01779399,0.00321362,0.000200608,0.0001385259],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1203087,0.0000551553,0.8732192,0.0007280161,0.005165119,0.0003044581,0.000002007566,0.000156781,0.00006055814],"genre_scores_gemma":[0.8484172,0.00001563664,0.1512496,0.00005600579,0.00007659294,0.000009810553,7.250077e-7,0.000005985153,0.000168421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9544906,"threshold_uncertainty_score":0.5596426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857456290386673,"score_gpt":0.3079053312848629,"score_spread":0.2693307683809961,"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."}}