{"id":"W2049819558","doi":"10.2501/ijmr-2013-052","title":"Discriminating between behaviour using market data from panels","year":2014,"lang":"en","type":"article","venue":"International Journal of Market Research","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Aggregate (composite); Variety (cybernetics); Panel data; Limiting; Sample (material); Estimation; Econometrics; Aggregate data; Sequence (biology); Market data; Market share; Marketing; Business; Economics; Microeconomics; Computer science; Statistics; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009889849,0.0001798721,0.0003116714,0.0009390433,0.0002141576,0.000962106,0.002774346,0.00008808849,0.004123612],"category_scores_gemma":[0.002837283,0.0001617822,0.0001174203,0.0003460976,0.0001180369,0.002099172,0.001871361,0.0007870439,0.00004026644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048074,"about_ca_system_score_gemma":0.00009649798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002168062,"about_ca_topic_score_gemma":0.0001094189,"domain_scores_codex":[0.996122,0.0002841526,0.0007418092,0.0003446187,0.002094684,0.0004127395],"domain_scores_gemma":[0.9957079,0.001565908,0.0005289308,0.0005134618,0.001632113,0.00005174095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002644479,0.00009899519,0.846908,0.00002719954,0.0001524913,0.0001133168,0.00004306449,0.000003377489,0.0009348778,0.00009829411,0.01833181,0.1330242],"study_design_scores_gemma":[0.00100123,0.00001827416,0.927703,0.0004211211,0.0001900538,0.00003271606,0.0004169939,0.01594215,0.00008399032,0.002786335,0.05113051,0.0002736843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592906,0.00007882502,0.002619028,0.001581474,0.001354177,0.0001327371,0.00006173537,0.00002336367,0.03485811],"genre_scores_gemma":[0.9927144,0.0000226494,0.001208973,0.0001253879,0.005382163,0.00000153753,0.00006864604,0.000036484,0.0004397413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1327505,"threshold_uncertainty_score":0.9967868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.249163947783168,"score_gpt":0.421923420887989,"score_spread":0.172759473104821,"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."}}