{"id":"W2064770256","doi":"10.1147/sj.402.0394","title":"Intelligent decision support for protein crystal growth","year":2001,"lang":"en","type":"article","venue":"IBM Systems Journal","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Queen's University; University Health Network; Ontario Institute for Cancer Research","funders":"University of Pittsburgh","keywords":"Correctness; Computer science; Software; Protein crystallization; Crystallization; Component (thermodynamics); Process (computing); Artificial intelligence; Algorithm; Engineering; Programming language; Chemical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001710261,0.0001600312,0.0002191583,0.000182951,0.0003875958,0.000662272,0.0006977037,0.00008830328,0.00003293224],"category_scores_gemma":[0.0001381354,0.0001290188,0.0001294747,0.0002224682,0.00001509242,0.0004767582,0.00007780553,0.0002690333,0.00005715074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009820854,"about_ca_system_score_gemma":0.0001840705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002664197,"about_ca_topic_score_gemma":0.000002020342,"domain_scores_codex":[0.9982671,0.0001018549,0.0005438959,0.0002654477,0.0003848187,0.0004368905],"domain_scores_gemma":[0.9988101,0.0001513443,0.0002767168,0.0002365819,0.0002860087,0.0002392644],"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":[0.001099785,0.0009489173,0.04609323,0.001090794,0.0006386407,0.003711378,0.01099062,0.0277347,0.02249144,0.09155937,0.2585766,0.5350646],"study_design_scores_gemma":[0.003360025,0.002820512,0.0009870064,0.002643623,0.0000460279,0.02440785,0.0004696145,0.3808038,0.003387942,0.02383791,0.5556548,0.00158091],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01545419,0.0003245296,0.9808676,0.000268929,0.001410967,0.000375317,0.000003876709,0.00009015368,0.001204389],"genre_scores_gemma":[0.9493099,0.00001541755,0.04851379,0.00008023422,0.000676579,0.00003587049,0.000003049105,0.00001790457,0.001347214],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9338558,"threshold_uncertainty_score":0.6386304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873250277522924,"score_gpt":0.2692422154708109,"score_spread":0.2405097126955817,"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."}}