{"id":"W1992756285","doi":"10.1016/s0098-1354(00)00594-9","title":"Smooth representation of trends by a wavelet-based technique","year":2000,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Representation (politics); Computer science; Artificial intelligence; Mathematics; Pattern recognition (psychology); Algorithm; Applied mathematics; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005754928,0.0004764936,0.0004613147,0.001168718,0.0002189868,0.0006872077,0.000451199,0.0007971514,0.001600637],"category_scores_gemma":[0.001467191,0.0003211777,0.000839968,0.001599429,0.0003601028,0.0008586559,0.0005249815,0.001136258,0.0008478913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487965,"about_ca_system_score_gemma":0.0003773664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004895682,"about_ca_topic_score_gemma":0.0006244212,"domain_scores_codex":[0.9998534,0.00002379035,0.00001285444,0.00002851838,0.00006557966,0.00001589953],"domain_scores_gemma":[0.9997063,0.00008516401,0.00003935539,0.00005245951,0.00009657232,0.00002013634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003806645,0.0001181614,0.0008416866,0.0003385144,0.0001452381,0.0002372092,0.0001662312,0.05282596,0.3741553,0.03234627,0.002274375,0.5361704],"study_design_scores_gemma":[0.00003615405,0.0002110462,0.002727999,0.00004489308,0.0001437161,0.0004334403,0.00005426025,0.9163309,0.05767558,0.01274703,0.009541908,0.0000530545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01522435,0.0002191303,0.9834186,0.00008936127,0.00007402674,0.00002138729,0.00005487728,0.0001664908,0.0007318003],"genre_scores_gemma":[0.1606465,0.001655856,0.8316203,0.00007836659,0.0001766776,0.00007719646,0.0002924372,0.0001955688,0.005256978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001600637,"threshold_uncertainty_score":0.005354643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009486446108222078,"score_gpt":0.2422462783919691,"score_spread":0.2327598322837471,"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."}}