{"id":"W4401398707","doi":"10.1007/s00521-024-10263-6","title":"Using machine learning techniques for the classification of ultra-low concentrations of cannabis in biological fluids","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Mitacs","keywords":"Computational Science and Engineering; Computer science; Cannabis; Artificial intelligence; Machine learning; Psychology; Psychiatry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005378958,0.0004367374,0.0004965215,0.001416342,0.0004079752,0.0008490979,0.0004126364,0.0009227915,0.0005924513],"category_scores_gemma":[0.001500128,0.000168265,0.0006309616,0.000720486,0.0004067326,0.0006211054,0.0004172143,0.0009517337,0.0004153667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004348696,"about_ca_system_score_gemma":0.0004949817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002918298,"about_ca_topic_score_gemma":0.00357205,"domain_scores_codex":[0.9997296,0.00005303763,0.00001852342,0.0000612115,0.00009439358,0.0000432966],"domain_scores_gemma":[0.9994352,0.0002893007,0.0000786599,0.00003318331,0.0001383578,0.00002524219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001253537,0.0006891299,0.03830593,0.0005648989,0.0003114362,0.00047741,0.0002996819,0.04020516,0.4662502,0.001850749,0.001823834,0.4479681],"study_design_scores_gemma":[0.00002938363,0.0003626566,0.01637848,0.00003953548,0.0001159763,0.0004084318,0.0001865729,0.8377537,0.1400976,0.002254615,0.002298907,0.00007417485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7697813,0.004496712,0.2186903,0.0007375455,0.0004296093,0.0001688365,0.0005858339,0.001194109,0.003915717],"genre_scores_gemma":[0.9265776,0.001018771,0.06943915,0.0001458935,0.00009655356,0.0000495659,0.000345129,0.0000299103,0.002297385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002918298,"threshold_uncertainty_score":0.005802631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03519639512153769,"score_gpt":0.3010577708596843,"score_spread":0.2658613757381466,"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."}}