{"id":"W4392305831","doi":"10.5220/0012422700003636","title":"Predicting Major Donor Prospects Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001197628,0.0004293982,0.0003324955,0.001677217,0.0003285141,0.001184863,0.0004422849,0.0006509527,0.003131478],"category_scores_gemma":[0.004883436,0.0001843935,0.0004350808,0.0009798645,0.0001725005,0.000903303,0.0003923927,0.0009641079,0.0011001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004441866,"about_ca_system_score_gemma":0.0006037606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003756163,"about_ca_topic_score_gemma":0.0063024,"domain_scores_codex":[0.9997817,0.00006002497,0.00001953483,0.00004282204,0.00003589482,0.00006008854],"domain_scores_gemma":[0.995201,0.00302653,0.0006400092,0.0001594411,0.0004851304,0.0004878433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004161655,0.0005709864,0.9150702,0.00002743553,0.0000793206,0.0001558244,0.0000406197,0.03310924,0.0002493449,0.000669094,0.00262692,0.04698495],"study_design_scores_gemma":[0.00003339105,0.0002920252,0.2531048,0.00004531029,0.0000847199,0.0002796139,0.0003262073,0.7366254,0.001154553,0.006358715,0.001664026,0.00003126602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899195,0.0004055926,0.005725167,0.0004912287,0.00004432846,0.00002214018,0.001215693,0.00009472633,0.002081486],"genre_scores_gemma":[0.9970255,0.0001456535,0.00102936,0.00001810277,0.00002689735,0.000007064627,0.001075587,0.000004178929,0.000667781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003756163,"threshold_uncertainty_score":0.01047581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003766559196816,"score_gpt":0.2501237715517431,"score_spread":0.230086105959775,"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."}}