{"id":"W4402971342","doi":"10.2139/ssrn.4972346","title":"Unsupervised Learning Based on Prior Knowledge Enhancement for the Classification and Evaluation of Geological–Engineering Sweet Spots","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Spots; Artificial intelligence; Computer science; Sweet spot; Pattern recognition (psychology); Chemistry; Simulation","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":[0.001319316,0.0006847277,0.001135845,0.002571071,0.0004064361,0.001318029,0.00134719,0.001451217,0.001808012],"category_scores_gemma":[0.003503785,0.0003669999,0.0009071062,0.001420638,0.0006757382,0.00153457,0.001535775,0.0009614941,0.0006752348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004525507,"about_ca_system_score_gemma":0.0007822399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003380778,"about_ca_topic_score_gemma":0.004270699,"domain_scores_codex":[0.9993543,0.0001406345,0.00004505318,0.0002246343,0.000136426,0.00009894629],"domain_scores_gemma":[0.9976752,0.001211568,0.0002288593,0.0003020448,0.0004397312,0.0001426386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001062732,0.0008032638,0.01340959,0.0002287752,0.0002690597,0.0002519814,0.000198939,0.2397205,0.03014506,0.005726901,0.004513458,0.7036697],"study_design_scores_gemma":[0.00001312609,0.00005905556,0.002308695,0.000009881212,0.00003174611,0.00005080199,0.00002579907,0.9900498,0.004233395,0.002918205,0.0002874193,0.00001194657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2445125,0.0006884055,0.7501457,0.00019391,0.0000574192,0.00009849688,0.0004721817,0.001466124,0.002365249],"genre_scores_gemma":[0.8865269,0.0001765643,0.1104565,0.00005943317,0.00006749681,0.00005826945,0.001047435,0.00009349472,0.001513815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003380778,"threshold_uncertainty_score":0.00697732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435925624987085,"score_gpt":0.266496266554804,"score_spread":0.2421370103049331,"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."}}