{"id":"W2295333720","doi":"10.14778/2856318.2856331","title":"CLAMShell","year":2015,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Crowds; Computer science; Latency (audio); Speedup; Data science; Computer security; Operating system; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0004279539,0.0001009866,0.0001229801,0.00004400447,0.00007156903,0.0001002133,0.0009290282,0.00003202793,0.000001859021],"category_scores_gemma":[0.00008328845,0.00006687511,0.00007377498,0.000280341,0.00005091353,0.0002108949,0.0004739838,0.00009788693,0.00001940746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005865012,"about_ca_system_score_gemma":0.00004125707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000277478,"about_ca_topic_score_gemma":6.030181e-7,"domain_scores_codex":[0.9989984,0.00000535525,0.0001859243,0.0002160249,0.0003822793,0.0002120261],"domain_scores_gemma":[0.9993323,0.00001539264,0.0001409924,0.0002149595,0.0001988977,0.00009744889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005061615,0.000540233,0.01641231,0.0002193049,0.0001242362,0.000004446753,0.01555516,0.0005165673,0.1741468,0.5860891,0.1397205,0.06662072],"study_design_scores_gemma":[0.001389168,0.0002368239,0.001530626,0.0002000244,0.00003045804,0.0001012793,0.0009489834,0.0106338,0.8909265,0.03914884,0.05444123,0.000412255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8262311,0.0004086636,0.006498457,0.00630544,0.001700504,0.0006295328,0.000001493938,0.0003941433,0.1578306],"genre_scores_gemma":[0.9911807,0.000003256044,0.007616778,0.0002082496,0.0000650924,0.000008926,8.145211e-8,0.000006904274,0.0009100611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7167797,"threshold_uncertainty_score":0.2727088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02041225764979892,"score_gpt":0.2083040614978376,"score_spread":0.1878918038480387,"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."}}