{"id":"W3048358675","doi":"","title":"Identification of Shifts and Trends in Hydrometric Data in Canada Based on Several Detection Tests","year":2004,"lang":"en","type":"article","venue":"AGU Spring Meeting Abstracts","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Econometrics; Computer science; Mathematics","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":[0.001578706,0.0002951009,0.0002291341,0.002136265,0.0009090392,0.0007844783,0.0006820435,0.0003760821,0.0005227326],"category_scores_gemma":[0.005911245,0.0001589513,0.0002290891,0.001500191,0.0007122973,0.0004361649,0.0004053426,0.0003611277,0.0001072546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005506991,"about_ca_system_score_gemma":0.007230151,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.842369,"about_ca_topic_score_gemma":0.8709177,"domain_scores_codex":[0.9993018,0.00006987809,0.00005350121,0.0001395548,0.0002587207,0.0001765361],"domain_scores_gemma":[0.9944115,0.001784747,0.0005322596,0.0001352949,0.002600427,0.0005357093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003685206,0.00009083778,0.9503556,0.00003336411,0.00006086522,0.00008134978,0.0003590505,0.004822135,0.006410845,0.0003061679,0.0004372619,0.03667411],"study_design_scores_gemma":[0.00001893084,0.00009082376,0.9589042,0.000007602462,0.00003815414,0.00005742808,0.0005011246,0.03601062,0.003638168,0.0001064244,0.0006035169,0.00002289081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976011,0.00006229921,0.001040225,0.00005551527,0.000002809013,0.0000138434,0.0004036113,0.00003488405,0.0007857496],"genre_scores_gemma":[0.9977417,0.00003481618,0.001363155,0.00001248069,0.000001708103,0.000005711367,0.0005023049,0.0000040417,0.000334144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.157631,"threshold_uncertainty_score":0.3171186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119060244734349,"score_gpt":0.2271469002770056,"score_spread":0.2152408758035707,"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."}}