{"id":"W2146689060","doi":"10.1111/rssc.12002","title":"Calendarization with Interpolating Splines and State Space Models","year":2013,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; State space; Spline (mechanical); Mathematics; Interpolation (computer graphics); Space (punctuation); Computer science; State-space representation; Algorithm; State (computer science); Applied mathematics; Statistics; Artificial intelligence","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":[0.008525902,0.0005664123,0.001109991,0.001760439,0.0006093348,0.001446993,0.001662111,0.001224674,0.002800689],"category_scores_gemma":[0.02702565,0.0005333124,0.001384437,0.003039401,0.001347363,0.002204426,0.001982874,0.002447862,0.0003622434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156209,"about_ca_system_score_gemma":0.001560902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009398381,"about_ca_topic_score_gemma":0.005024594,"domain_scores_codex":[0.9971213,0.001815525,0.0001354959,0.0003662238,0.0003888583,0.00017264],"domain_scores_gemma":[0.9878682,0.008972545,0.001078275,0.001148865,0.000689358,0.0002427826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008195981,0.00003364288,0.002526427,0.00004827731,0.00003974873,0.00004986883,0.000118808,0.823936,0.000319884,0.1352008,0.0007156482,0.03692897],"study_design_scores_gemma":[0.00000436675,0.000007497683,0.0002393355,0.00000724822,0.000002909066,0.00000516328,0.000008626292,0.960842,0.0001371501,0.03821389,0.0005237715,0.000008020233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01445259,0.0001520977,0.9842216,0.0002017836,0.00002920336,0.00001593385,0.0001212052,0.0001987124,0.0006068983],"genre_scores_gemma":[0.5866827,0.0006045545,0.4070128,0.0001012902,0.0001333257,0.0001851044,0.0009052008,0.0002484882,0.004126596],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009398381,"threshold_uncertainty_score":0.04508984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003719353645809946,"score_gpt":0.1845555676166046,"score_spread":0.1808362139707946,"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."}}