{"id":"W2123382753","doi":"10.1109/das.2012.51","title":"Linear Compression of Digital Ink via Point Selection","year":2012,"lang":"en","type":"article","venue":"","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Compression ratio; Compression (physics); Computer science; Data compression; Algorithm; Piecewise linear function; Point (geometry); Data compression ratio; Inkwell; Linear approximation; Piecewise; Selection (genetic algorithm); Mathematics; Artificial intelligence; Image compression; Nonlinear system; Speech recognition; Image processing; Materials science; Mathematical analysis; Engineering; Geometry","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":[0.0001041152,0.00007298282,0.00008678866,0.00007204697,0.00002757767,0.0001234382,0.0003316944,0.00003405555,0.000008309352],"category_scores_gemma":[0.00003119134,0.00005858927,0.00003085679,0.0002450174,0.00003113668,0.003801433,0.0002717248,0.00005663492,0.00003284222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000016051,"about_ca_system_score_gemma":0.00001427192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005445329,"about_ca_topic_score_gemma":1.66552e-7,"domain_scores_codex":[0.9993918,0.000008956548,0.0001524662,0.0001206485,0.0001615793,0.0001645561],"domain_scores_gemma":[0.9995629,0.00002318648,0.00006971566,0.0001890479,0.00009324503,0.00006191811],"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":[0.00001562673,0.001022055,0.02071778,0.00009452357,0.00002176977,0.000001552927,0.0007475187,0.00001261917,0.08036673,0.03461589,0.009310806,0.8530731],"study_design_scores_gemma":[0.0001276352,0.0001321365,0.001295911,0.00004753103,0.000002857769,0.00004473558,0.000006626804,0.06344433,0.9126582,0.01944346,0.002577833,0.0002187378],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005364473,0.00003301616,0.9649981,0.0001099458,0.00006732292,0.00005338361,4.303447e-7,0.0005392808,0.02883407],"genre_scores_gemma":[0.7784902,6.162263e-7,0.2211469,0.00004971071,0.00002654147,0.000002299558,0.000001051013,0.000004542317,0.0002781295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8528544,"threshold_uncertainty_score":0.2755947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769240014586346,"score_gpt":0.2726952986035409,"score_spread":0.2550028984576774,"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."}}