{"id":"W4405788218","doi":"10.1109/iros58592.2024.10802173","title":"Working Backwards: Learning to Place by Picking","year":2024,"lang":"en","type":"article","venue":"","topic":"Engineering Education and Pedagogy","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Human–computer interaction","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":[0.001259799,0.0009552522,0.0007478081,0.0003661683,0.0004805212,0.0005608078,0.002156716,0.0009271658,0.003310094],"category_scores_gemma":[0.005681358,0.0006532074,0.0005882218,0.0002916374,0.001078563,0.00150797,0.001722485,0.001682066,0.001213741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005273462,"about_ca_system_score_gemma":0.001914436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004449556,"about_ca_topic_score_gemma":0.006164024,"domain_scores_codex":[0.9992644,0.0001739959,0.00003810479,0.0003082022,0.0001430024,0.00007233005],"domain_scores_gemma":[0.9973829,0.001346808,0.000254807,0.0005841245,0.0001940663,0.0002372889],"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":[0.0006577148,0.000897585,0.01136563,0.0003295781,0.0001037003,0.0002586343,0.0006204344,0.2965365,0.04491188,0.006982447,0.005338824,0.631997],"study_design_scores_gemma":[0.00006482382,0.0004847296,0.00192307,0.00003464277,0.00002470439,0.0001180555,0.00009229495,0.9575108,0.02164222,0.01379341,0.004260017,0.00005119943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09906143,0.0002014067,0.8888757,0.0003874954,0.00008679006,0.0002153011,0.0003223003,0.007665017,0.003184551],"genre_scores_gemma":[0.6279748,0.000208694,0.3648746,0.0003441738,0.0000394635,0.0004325472,0.0007698786,0.0003449835,0.005010778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004449556,"threshold_uncertainty_score":0.01107335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151600127393729,"score_gpt":0.2539108105956707,"score_spread":0.2423948093217334,"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."}}