{"id":"W7075739555","doi":"","title":"Advances in conceptual modeling - applications and challenges ER 2010 workshops ACM-L, CMLSA, CMS, DE@ER, FP-UML, SeCoGIS, WISM, Vancouver, BC, Canada, November 1 - 4, 2010 ; proceedings","year":2010,"lang":"en","type":"article","venue":"","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Conceptual model; Work (physics); Field (mathematics); Key (lock); Data modeling","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":[0.0268281,0.00112537,0.001846755,0.004100924,0.001779897,0.01380979,0.004514578,0.0034415,0.01273392],"category_scores_gemma":[0.0281071,0.001980787,0.002069817,0.007638807,0.004248497,0.02303643,0.007202549,0.006577042,0.003501387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006844497,"about_ca_system_score_gemma":0.007022401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02982183,"about_ca_topic_score_gemma":0.03495979,"domain_scores_codex":[0.9912321,0.005074125,0.0007376323,0.0007382558,0.001921204,0.0002966746],"domain_scores_gemma":[0.9739216,0.0163872,0.0004322403,0.004132953,0.004038449,0.001087578],"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":[0.00005047085,0.0001437542,0.001407169,0.0006195131,0.00007453768,0.0001261482,0.001797952,0.005981135,0.0006327835,0.6119965,0.09964324,0.2775269],"study_design_scores_gemma":[0.00001503196,0.00002249981,0.000693516,0.0008322283,0.00004530779,0.0002735718,0.002157049,0.0415814,0.0005878255,0.2932697,0.6604744,0.00004763173],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01186978,0.1094257,0.6549647,0.1563548,0.003876768,0.0001407179,0.001211787,0.002723967,0.05943189],"genre_scores_gemma":[0.1035695,0.1295659,0.7170424,0.005732423,0.003007749,0.0002493454,0.004076739,0.001733117,0.03502281],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02982183,"threshold_uncertainty_score":0.1418822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255256649586455,"score_gpt":0.2665233160984916,"score_spread":0.233970749602627,"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."}}