{"id":"W7027767878","doi":"","title":"Diagnostisten lääkeaineiden hyödyntäminen työelämässä : Kyselytutkimus rajattuun lääkkeenmääräämiseen oikeutetuille optikoille","year":2019,"lang":"fi","type":"other","venue":"Theseus (Ammattikorkeakoulujen)","topic":"Plant-Microbe Interactions and Immunity","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Statistical analysis; Power (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009743803,0.0006116909,0.0006696768,0.001179567,0.001171474,0.001800245,0.0004864511,0.001202174,0.00882472],"category_scores_gemma":[0.001225759,0.0003716716,0.0006644923,0.0007114147,0.0005480515,0.0008573777,0.001101271,0.0014777,0.002357712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001381219,"about_ca_system_score_gemma":0.001572577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0116218,"about_ca_topic_score_gemma":0.0196939,"domain_scores_codex":[0.9990242,0.0001428731,0.00007913594,0.0001826112,0.0003899887,0.0001812888],"domain_scores_gemma":[0.9992588,0.0001588936,0.000143754,0.00003335428,0.0002951509,0.0001099761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005431447,0.001010666,0.3868923,0.001554747,0.0003148243,0.008189056,0.004820392,0.000366133,0.2271001,0.00149374,0.00809029,0.3547362],"study_design_scores_gemma":[0.000159288,0.004047235,0.5985355,0.001011943,0.0005954391,0.02537605,0.01428443,0.001185045,0.222416,0.002125961,0.1300713,0.0001918496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9180176,0.02590745,0.006663126,0.002358297,0.0005270918,0.0005729298,0.00169448,0.0001970406,0.04406194],"genre_scores_gemma":[0.8733088,0.01629998,0.01159828,0.001392809,0.000205251,0.0004058991,0.002192565,0.0001256429,0.09447066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0116218,"threshold_uncertainty_score":0.02952158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240479772998361,"score_gpt":0.2388142610642719,"score_spread":0.2164094633342883,"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."}}