{"id":"W1921128344","doi":"10.1002/sta4.74","title":"Spanifold: spanning tree flattening onto lower dimension","year":2015,"lang":"en","type":"article","venue":"Stat","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Isomap; Intrinsic dimension; Mathematics; Data point; Nonlinear dimensionality reduction; Pairwise comparison; Minimum spanning tree; Flattening; Tree (set theory); Dimension (graph theory); Hessian matrix; Manifold (fluid mechanics); Curse of dimensionality; Energy minimization; Embedding; Algorithm; Combinatorics; Computer science; Artificial intelligence; Statistics","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":[0.0001886991,0.00009461818,0.00009726129,0.00006306917,0.00007567155,0.0001022334,0.0002359959,0.00004190384,0.00002717431],"category_scores_gemma":[0.00003378115,0.00007961581,0.00003548491,0.0001434221,0.00001257377,0.0005627139,0.0001781857,0.00008678878,0.0004508484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003287936,"about_ca_system_score_gemma":0.00003882305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004817963,"about_ca_topic_score_gemma":0.00001991823,"domain_scores_codex":[0.9990892,0.00003459879,0.0001233034,0.0002553661,0.0002682231,0.0002293221],"domain_scores_gemma":[0.9994178,0.00002974974,0.00004675822,0.0002750521,0.00008036346,0.0001503206],"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":[0.0001462553,0.0002682626,0.005758993,0.00003384339,0.00004995837,0.0004370394,0.01236311,0.001107653,0.08310971,0.005564684,0.4434043,0.4477561],"study_design_scores_gemma":[0.007698391,0.002041775,0.01243094,0.001181207,0.00006536326,0.0001875168,0.003549656,0.3455347,0.1295941,0.0442803,0.4505537,0.002882396],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6068421,0.000228032,0.3670577,0.001749567,0.002059903,0.0001620547,0.000002317907,0.0004217647,0.02147658],"genre_scores_gemma":[0.9520707,0.000005820404,0.04562007,0.0006239064,0.0000914362,0.000005831853,0.000006183918,0.00001014488,0.001565938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4448738,"threshold_uncertainty_score":0.5794894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618122214182163,"score_gpt":0.2608451679219877,"score_spread":0.2246639457801661,"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."}}