{"id":"W2130437743","doi":"10.1109/igarss.1989.567186","title":"Image Analysis On A Macintosh II.","year":2005,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer graphics (images); Image (mathematics); Computer vision; Artificial intelligence","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":[0.0009038209,0.0009510878,0.000885723,0.002314518,0.0008620597,0.002226186,0.001013543,0.0006761389,0.1991909],"category_scores_gemma":[0.002898169,0.0004707129,0.0007071832,0.001583478,0.0003887485,0.001542562,0.001177183,0.001316836,0.0245832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003355375,"about_ca_system_score_gemma":0.0009259727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00189214,"about_ca_topic_score_gemma":0.002808378,"domain_scores_codex":[0.9995502,0.00005122974,0.00004823805,0.0001360896,0.0001464416,0.00006783021],"domain_scores_gemma":[0.9987971,0.000273545,0.0000503594,0.0002412725,0.0005488278,0.00008901003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001016923,0.0002607362,0.002503918,0.001274935,0.0001922517,0.0004059445,0.0006712062,0.00181554,0.1637738,0.01024145,0.1994238,0.6184195],"study_design_scores_gemma":[0.000413,0.0005983456,0.02986821,0.0005357569,0.0003595304,0.004166723,0.001149799,0.1304239,0.2653859,0.02895251,0.5378157,0.0003306178],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0323908,0.0006151637,0.8215822,0.0008529673,0.000782959,0.0010894,0.00996589,0.08755188,0.04516875],"genre_scores_gemma":[0.08553913,0.001056834,0.8238791,0.00060083,0.0001636093,0.001560073,0.009653728,0.01285598,0.06469075],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1991909,"threshold_uncertainty_score":0.66636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304558306445269,"score_gpt":0.299099527121728,"score_spread":0.2860539440572753,"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."}}