{"id":"W2030723399","doi":"10.1016/j.comgeo.2008.06.004","title":"A linear-space algorithm for distance preserving graph embedding","year":2008,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"National Research Council Canada; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Mathematics; Scaling; Embedding; Combinatorics; Euclidean distance; Algorithm; Diagonal; Multidimensional scaling; Discrete mathematics; Computer science; Statistics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001499123,0.0001235681,0.0001365927,0.000203092,0.0003612749,0.00006168902,0.0004377491,0.00005453399,0.00001959511],"category_scores_gemma":[0.00005256917,0.0001236408,0.00009740376,0.000673065,0.00004097709,0.000522893,0.0001540084,0.00009817669,0.00004654734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002413322,"about_ca_system_score_gemma":0.00005156497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003365268,"about_ca_topic_score_gemma":2.202632e-7,"domain_scores_codex":[0.9988304,0.00002708991,0.000199494,0.0003601316,0.0003364742,0.0002463553],"domain_scores_gemma":[0.9989835,0.000385693,0.00009001997,0.0002020669,0.0002443687,0.00009436411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000472358,0.0004866176,0.001567603,0.0001539183,0.0001209183,0.00007842071,0.001319997,0.438308,0.0006196037,0.02403528,0.1022012,0.4310612],"study_design_scores_gemma":[0.0004503312,0.0000468944,0.001138119,0.00004226165,0.000002120071,0.00003463393,0.00002031345,0.9683598,0.0003938254,0.01834678,0.01097906,0.0001858157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009690645,0.0003618994,0.9885824,0.0003777021,0.0003364863,0.0002283874,0.00002236549,0.0001645125,0.0002356049],"genre_scores_gemma":[0.1164384,0.00002969864,0.8824446,0.0003266172,0.0001579152,0.00008995509,0.00006501364,0.00001231264,0.0004354909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5300518,"threshold_uncertainty_score":0.5041925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02632108000035872,"score_gpt":0.2847886276833322,"score_spread":0.2584675476829735,"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."}}