{"meta":{"query_hash":"8aef512b5477","filters":{"venue":"Advances in Adaptive Data Analysis"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/8aef512b5477","api":"https://metacan.xera.ac/api/v1/cohort?venue=Advances+in+Adaptive+Data+Analysis"},"results":[{"id":"W1976915567","doi":"10.1142/s1793536913500015","title":"EOF-MSE ADAPTIVE METHOD TO ASSESS AN ACID DEPOSITION MONITORING NETWORK OVER ALBERTA, CANADA","year":2013,"lang":"en","type":"article","venue":"Advances in Adaptive Data Analysis","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"National Science Foundation","keywords":"Mean squared error; Statistics; Sampling (signal processing); Mathematics; Environmental science; Geography; Computer science; Telecommunications","score_opus":0.019733298368314566,"score_gpt":0.2866966226174634,"score_spread":0.26696332424914887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976915567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64364064,0.00046129012,0.34200463,0.00026643922,0.000045013814,0.00027197736,0.0051451363,0.0019314669,0.0062334477],"genre_scores_gemma":[0.7613196,0.00014959014,0.23064403,0.000073690455,0.000018075196,0.00013919726,0.0050568283,0.00011714767,0.0024819563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99936,0.000094037256,0.000036882248,0.00014151576,0.00028672483,0.00008079528],"domain_scores_gemma":[0.9987716,0.0002441432,0.00011721158,0.000062830266,0.00075956446,0.000044668286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012956811,0.0005624764,0.00034867323,0.0023369151,0.0005270857,0.0006618324,0.00087141263,0.0003547154,0.00089366577],"category_scores_gemma":[0.0044189803,0.00017815083,0.00030806745,0.0025415898,0.0002705679,0.00031091154,0.00048325467,0.00038969915,0.0001371272],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025885284,0.00012048842,0.29909745,0.00013094094,0.00023734619,0.0003327493,0.00032607384,0.36748546,0.009388544,0.0026273094,0.0052037374,0.3147911],"study_design_scores_gemma":[0.000018483775,0.000021421893,0.097221166,0.0000147382225,0.000026959115,0.000054854117,0.00019113274,0.89724576,0.0016303478,0.00081007683,0.0027411068,0.00002398388],"about_ca_topic_score_codex":0.6673615,"about_ca_topic_score_gemma":0.68843794,"teacher_disagreement_score":0.3326385,"about_ca_system_score_codex":0.002405447,"about_ca_system_score_gemma":0.003989983,"threshold_uncertainty_score":0.66919494},"labels":[],"label_agreement":null},{"id":"W2005387402","doi":"10.1142/s1793536911000751","title":"TREND FILTERING: EMPIRICAL MODE DECOMPOSITIONS VERSUS ℓ<sub>1</sub> AND HODRICK–PRESCOTT","year":2011,"lang":"en","type":"article","venue":"Advances in Adaptive Data Analysis","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Hilbert–Huang transform; Hodrick–Prescott filter; Mode (computer interface); Series (stratigraphy); Time series; Trend analysis; Lag; Mathematics; Computer science; Algorithm; Econometrics; Statistics","score_opus":0.0918236351250181,"score_gpt":0.34323980978280594,"score_spread":0.25141617465778787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005387402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01122126,0.00082728936,0.9864442,0.00019878052,0.00007098818,0.000025107547,0.00006421056,0.00017353096,0.0009745644],"genre_scores_gemma":[0.18043238,0.0023506198,0.812267,0.00018792308,0.00032996506,0.00013274778,0.0003653805,0.0001580426,0.0037759277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995326,0.00011579474,0.00004889157,0.00012092313,0.00015139123,0.000030464833],"domain_scores_gemma":[0.998367,0.00092166994,0.00019172253,0.00023198647,0.00025510555,0.000032570937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002146031,0.00097446755,0.00069814554,0.001516628,0.00030561336,0.0010275989,0.0007249007,0.0009890236,0.0023999503],"category_scores_gemma":[0.0072447644,0.00038063945,0.00088914717,0.0022445247,0.00059277803,0.0022638699,0.00064872805,0.0014312146,0.0006937932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025707847,0.00009355581,0.005748979,0.00033413913,0.00019757733,0.00026163863,0.00030459496,0.07749712,0.01899466,0.067597605,0.0030149296,0.8256982],"study_design_scores_gemma":[0.00003095437,0.00014358327,0.007699464,0.00007626028,0.00008036905,0.00029957417,0.000107448075,0.9305582,0.009219007,0.041101478,0.010617455,0.000066263034],"about_ca_topic_score_codex":0.001342048,"about_ca_topic_score_gemma":0.0014193193,"teacher_disagreement_score":0.0023999503,"about_ca_system_score_codex":0.00035936627,"about_ca_system_score_gemma":0.00043635478,"threshold_uncertainty_score":0.01134944},"labels":[],"label_agreement":null},{"id":"W2080146545","doi":"10.1142/s1793536914500071","title":"AUTOCORRELATION IN SHORT TIME SERIES WITH TRENDS: A SIMULATION STUDY OF ESTIMATION AND SIGNIFICANCE TESTING WITH APPLICATION TO AIR QUALITY DATA","year":2014,"lang":"en","type":"article","venue":"Advances in Adaptive Data Analysis","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Environment and Protected Areas","funders":"","keywords":"Autocorrelation; Estimator; Autoregressive model; Econometrics; Statistics; Series (stratigraphy); Time series; Estimation; Statistical hypothesis testing; Mathematics; Engineering","score_opus":0.050279808008363146,"score_gpt":0.34364801987329713,"score_spread":0.293368211864934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080146545","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9624441,0.00054692815,0.035015557,0.0003869485,0.00003050343,0.00005676941,0.00017762459,0.0000825431,0.0012589676],"genre_scores_gemma":[0.99300164,0.00013167181,0.0063142492,0.000023840012,0.0000087921,0.000047664907,0.00017761097,0.000016067626,0.00027848248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757916,0.0017886757,0.000098153985,0.0001975246,0.00020085418,0.00013572715],"domain_scores_gemma":[0.89595085,0.09638493,0.0024390412,0.0021505328,0.0025265114,0.00054811576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012097302,0.00043035788,0.0008322452,0.0011068163,0.0005765299,0.00072404876,0.00079115224,0.0011508113,0.0009310113],"category_scores_gemma":[0.036735903,0.00027787304,0.0014722589,0.0013955049,0.00091043377,0.0012244951,0.0006211477,0.0017466905,0.00008989193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041582133,0.00032793925,0.06105365,0.00012951363,0.00027699093,0.0005063193,0.00033407815,0.9083791,0.0010107346,0.013583239,0.0011654515,0.012817192],"study_design_scores_gemma":[0.000030230627,0.0001482508,0.0071176803,0.000014720016,0.000037765265,0.00005893512,0.000097299635,0.9891195,0.00026534707,0.002904282,0.00019055158,0.000015498923],"about_ca_topic_score_codex":0.018835781,"about_ca_topic_score_gemma":0.01042943,"teacher_disagreement_score":0.018835781,"about_ca_system_score_codex":0.0009583494,"about_ca_system_score_gemma":0.0007587984,"threshold_uncertainty_score":0.06397742},"labels":[],"label_agreement":null}]}