{"id":"W2016859188","doi":"10.1021/ac070961x","title":"Power Law Analysis Estimates of Analyte Concentration and Particle Size in Highly Scattering Granular Samples from Photon Time-of-Flight Measurements","year":2007,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Chemistry; Scattering; Particle size; Power law; Computational physics; Absorption (acoustics); Light scattering; Particle (ecology); Photon; Fractal dimension; Dynamic light scattering; Molecular physics; Particle-size distribution; Analytical Chemistry (journal); Sample size determination; Optics; Statistical physics; Statistics; Fractal; Physics; Chromatography; Nanoparticle; Quantum mechanics; Mathematical analysis; Mathematics","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":[0.001546906,0.0004988969,0.0004651547,0.001826094,0.000184854,0.0006640441,0.0005131997,0.0004541618,0.0006838992],"category_scores_gemma":[0.008967087,0.000242751,0.0004840691,0.0007809433,0.000453673,0.001090039,0.0002974263,0.0005506806,0.0003930252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006203268,"about_ca_system_score_gemma":0.0003030309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002199063,"about_ca_topic_score_gemma":0.001327221,"domain_scores_codex":[0.9993505,0.0001208371,0.00004457682,0.0001591227,0.0002914802,0.00003343298],"domain_scores_gemma":[0.9940385,0.004341037,0.0005676331,0.0005066586,0.0005101038,0.00003610365],"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":[0.0005418245,0.0002388643,0.02407867,0.0003918124,0.0002108492,0.0005943184,0.0003229325,0.4970253,0.1770899,0.01063207,0.001564779,0.2873085],"study_design_scores_gemma":[0.000005419457,0.00004749037,0.005786373,0.000006584829,0.00001382787,0.0001204125,0.00001434933,0.9702267,0.02090906,0.002468617,0.0003799246,0.00002124417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1721726,0.0002593489,0.8250882,0.00004822091,0.00001264563,0.00008343873,0.0002341535,0.001041416,0.001059924],"genre_scores_gemma":[0.8017872,0.0003533739,0.1957999,0.0000430511,0.00001744394,0.0001462725,0.0006875927,0.0001409606,0.001024182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002199063,"threshold_uncertainty_score":0.008180916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809138617053267,"score_gpt":0.2419508596786443,"score_spread":0.2238594735081116,"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."}}