{"id":"W6981708817","doi":"","title":"Explaining variation in data warehouse usage, an interpretation perspective","year":2000,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Variation (astronomy); Interpretation (philosophy); Data warehouse","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.003989081,0.0008179184,0.0006474446,0.003624869,0.001587512,0.006009013,0.001807697,0.0007644424,0.003960184],"category_scores_gemma":[0.02099192,0.0006528013,0.0007859018,0.01330724,0.002457098,0.002511851,0.0009191831,0.00192962,0.0004468784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01293477,"about_ca_system_score_gemma":0.01694792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8556184,"about_ca_topic_score_gemma":0.8507069,"domain_scores_codex":[0.9979414,0.00082343,0.00009050244,0.0003293186,0.000601555,0.0002136752],"domain_scores_gemma":[0.9872208,0.008399329,0.0007691691,0.001115139,0.002284809,0.0002107125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.000318658,0.0002747378,0.3020572,0.0004253474,0.0004003576,0.0007257786,0.01686394,0.03972055,0.002165828,0.1741526,0.08667379,0.3762213],"study_design_scores_gemma":[0.00009261043,0.00006165502,0.4303756,0.0004958973,0.0004606148,0.0004050616,0.02019459,0.1689506,0.00503295,0.2519447,0.1217836,0.0002022368],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4158296,0.005746497,0.3393501,0.04609067,0.0002344353,0.0003433264,0.04199908,0.003997649,0.1464087],"genre_scores_gemma":[0.9164717,0.001198837,0.05961922,0.0008723179,0.00007453042,0.00009557664,0.007943455,0.0008082062,0.01291618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8556184,"threshold_uncertainty_score":0.2904637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007757196075291521,"score_gpt":0.1868953580388627,"score_spread":0.1791381619635712,"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."}}