{"id":"W6912655331","doi":"10.5281/zenodo.4603615","title":"RegCM 4 model Reference Manual","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Reference data; Climate model; Work (physics); Context (archaeology); Process (computing)","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":[0.001183789,0.001729932,0.001267619,0.001874658,0.00102433,0.001873567,0.005281838,0.002510787,0.1320514],"category_scores_gemma":[0.004106414,0.001340571,0.00232176,0.004158767,0.0003634275,0.002955569,0.001094187,0.00262426,0.07843392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001466768,"about_ca_system_score_gemma":0.00274253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04979037,"about_ca_topic_score_gemma":0.03098072,"domain_scores_codex":[0.999355,0.0001510236,0.00005724742,0.0001076625,0.0002582356,0.00007074529],"domain_scores_gemma":[0.9983513,0.00036783,0.0000691688,0.0002329066,0.0009192827,0.00005959881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001255655,0.00005226218,0.0009385047,0.0008310703,0.0001366898,0.000089541,0.00006729464,0.1154693,0.001207221,0.00885839,0.8388193,0.03340486],"study_design_scores_gemma":[0.0002626191,0.00002557564,0.00110129,0.0003591964,0.00009660756,0.0000734948,0.00008809499,0.07750422,0.002505833,0.02015042,0.897701,0.0001317217],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.003792735,0.001216207,0.08698335,0.001046167,0.001147734,0.0004631957,0.7904042,0.04135032,0.07359606],"genre_scores_gemma":[0.032006,0.002010793,0.1044489,0.001108495,0.000348866,0.002461318,0.7828342,0.03010924,0.0446723],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1320514,"threshold_uncertainty_score":0.4417561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2296532859997744,"score_gpt":0.3666760191803845,"score_spread":0.1370227331806101,"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."}}