{"id":"W2303810353","doi":"10.1007/978-3-319-25388-6","title":"Lectures on the Nearest Neighbor Method","year":2015,"lang":"en","type":"book","venue":"Springer series in the data sciences","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":224,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"k-nearest neighbors algorithm; Nearest neighbor search; Computer science; Artificial intelligence","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":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.008113025,0.000277129,0.0002303598,0.0001660737,0.0005866836,0.001393992,0.01771542,0.0001169078,0.0000330455],"category_scores_gemma":[0.001281984,0.0001343022,0.00003736129,0.0006026211,0.0006503469,0.001043423,0.002424532,0.0008146859,0.0001474188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000555139,"about_ca_system_score_gemma":0.0007268412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001373862,"about_ca_topic_score_gemma":0.000291833,"domain_scores_codex":[0.996571,0.0006609507,0.0002889086,0.001015695,0.001109781,0.0003536838],"domain_scores_gemma":[0.9940485,0.001187156,0.0002692978,0.00439339,0.0000523641,0.00004932281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006871702,0.00001856305,0.00003446702,0.00001338054,0.00000850687,0.000008351412,0.001032245,0.0001656653,0.000003906885,0.713353,0.2606696,0.02468532],"study_design_scores_gemma":[0.00004330619,0.0001083398,0.0004264569,0.0000615657,0.000007928331,0.00002019576,0.0001184101,0.009460178,0.000009278472,0.04511518,0.9444109,0.0002182768],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002767266,0.001781214,0.04567814,0.06748308,0.002277619,0.0008098871,0.0004237778,0.0002670189,0.8812516],"genre_scores_gemma":[0.01240363,0.002011208,0.3802884,0.03392128,0.006487055,0.000423913,0.004212332,0.000223794,0.5600284],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6837412,"threshold_uncertainty_score":0.9996427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1236141954165152,"score_gpt":0.3532936935617726,"score_spread":0.2296794981452575,"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."}}