{"id":"W4254624012","doi":"10.1145/503387.503388","title":"Acquisition of expanding targets","year":2002,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fitts's law; Computer science; Selection (genetic algorithm); Task (project management); Focus (optics); Target acquisition; Human–computer interaction; Artificial intelligence; Engineering; Systems engineering","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":[0.00002745598,0.00003201209,0.0000443407,0.00004126371,0.00002408518,0.00001191801,0.0001550942,0.00001134638,0.0006714644],"category_scores_gemma":[0.000005315843,0.00002705112,0.00003009173,0.00007962735,0.000007812047,0.0003793008,0.00004120647,0.00002085664,0.0001629882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008384368,"about_ca_system_score_gemma":0.000001280577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005748905,"about_ca_topic_score_gemma":1.786758e-7,"domain_scores_codex":[0.9997,0.000009665296,0.00006362842,0.00008401615,0.0000669748,0.00007573785],"domain_scores_gemma":[0.9997835,0.00001892503,0.00002735119,0.0001106443,0.00004387068,0.0000157071],"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":[0.000003457199,0.0001216174,0.000870615,0.000008971552,0.00002089204,0.000007791277,0.002043042,0.00001040356,0.7221397,0.2463554,0.02391849,0.004499649],"study_design_scores_gemma":[0.0001856714,0.00009428705,0.006859615,0.00001374175,0.000001999532,0.000006770031,0.000118902,0.02314294,0.9672311,0.001158313,0.001087945,0.00009871743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03040586,0.00008160083,0.8106385,0.0003979423,0.0002226914,0.00003792978,5.914106e-7,0.00001284889,0.158202],"genre_scores_gemma":[0.9958016,0.000005105809,0.003036521,0.0003028337,0.0000139783,0.000001032434,4.015693e-7,0.000001199935,0.0008373412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9653957,"threshold_uncertainty_score":0.7352064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865669958022965,"score_gpt":0.2532912065322731,"score_spread":0.2346345069520435,"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."}}