{"id":"W1493469155","doi":"10.1007/11788034_23","title":"Finding Faces in Gray Scale Images Using Locally Linear Embeddings","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Grayscale; Artificial intelligence; Computer vision; Scale (ratio); Computer graphics (images); Pattern recognition (psychology); Image (mathematics); Cartography; Geography","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":[0.0002656704,0.0009438876,0.001199675,0.001437141,0.0003001204,0.001155244,0.0009621374,0.0007879512,0.005663664],"category_scores_gemma":[0.001262942,0.0006165078,0.0008433038,0.001342723,0.0005347339,0.002221971,0.0014177,0.0008803835,0.002521791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000303613,"about_ca_system_score_gemma":0.0003023844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547301,"about_ca_topic_score_gemma":0.002388571,"domain_scores_codex":[0.9997137,0.00003592967,0.00001492056,0.00009712009,0.00009008459,0.00004826974],"domain_scores_gemma":[0.9996282,0.0001352007,0.00004343488,0.00009006133,0.0000727532,0.00003036868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003431678,0.0001427399,0.002093134,0.0002703308,0.00007025914,0.0001772633,0.0001771756,0.03039737,0.09387019,0.005785164,0.00545083,0.8612224],"study_design_scores_gemma":[0.00005303881,0.0003499587,0.004249908,0.00006147126,0.00009095578,0.0009156553,0.0006182643,0.9008116,0.05360475,0.03412983,0.005061207,0.00005337925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08021849,0.0005633688,0.9136722,0.0002010635,0.00005373967,0.0001140504,0.0003278141,0.002357553,0.002491669],"genre_scores_gemma":[0.3719095,0.0009149274,0.6190335,0.0001166484,0.00008126694,0.0001308468,0.00105992,0.0004708044,0.006282665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005663664,"threshold_uncertainty_score":0.01894683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832954711762967,"score_gpt":0.2640246846201225,"score_spread":0.2456951375024928,"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."}}