{"id":"W6982382540","doi":"","title":"Improving precipitation estimates from dual-wavelength radars","year":2003,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Policing Practices and Perceptions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Precipitation; Radar; Climate change; Process (computing)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00154431,0.0006685202,0.0006509831,0.0003591292,0.00420248,0.000416846,0.0005480028,0.001123361,0.001593574],"category_scores_gemma":[0.00339831,0.0007692633,0.0003464835,0.0006508417,0.0001347925,0.001922653,0.00005710222,0.001536118,0.0007489552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004023,"about_ca_system_score_gemma":0.0002863905,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1196918,"about_ca_topic_score_gemma":0.1362391,"domain_scores_codex":[0.9950177,0.0009114143,0.0008431273,0.001074562,0.001172234,0.0009809806],"domain_scores_gemma":[0.996205,0.00122259,0.001012422,0.0006104108,0.0004674173,0.0004821563],"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":[0.0002472061,0.0005230132,0.0001709759,0.0002923778,0.0005373634,0.00004234801,0.00392977,0.00006259975,0.04015855,0.1708925,0.000142842,0.7830005],"study_design_scores_gemma":[0.002851762,0.0004509055,0.01391735,0.001612655,0.00259923,0.00001973046,0.04298807,0.0003159691,0.02287578,0.1403575,0.7657864,0.006224636],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7622934,0.0001439993,6.650229e-7,0.0000557088,0.002967008,0.0006637334,0.001143026,0.0003478672,0.2323846],"genre_scores_gemma":[0.9464099,0.001134591,0.00607167,0.0004276275,0.0003558546,0.0001696948,0.002687005,0.0002523797,0.04249121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7767758,"threshold_uncertainty_score":0.9994758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03165383026396534,"score_gpt":0.3136589776685158,"score_spread":0.2820051474045505,"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."}}