{"id":"W2788372920","doi":"10.5539/ibr.v11n4p1","title":"Research on Mission Pricing of Crowdsourcing APP","year":2018,"lang":"en","type":"article","venue":"International Business Research","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Task (project management); Computer science; Cluster analysis; Order (exchange); Focus (optics); Pricing strategies; Operations research; Data science; Business; Marketing; Economics; Machine learning; World Wide Web; Finance; Management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005229625,0.000132362,0.0001754004,0.001587479,0.0005940557,0.000401324,0.001997086,0.0001081637,0.0001055887],"category_scores_gemma":[0.001515231,0.0001169472,0.00005315512,0.002892452,0.0005385084,0.000366302,0.001013921,0.0006630784,0.0003359944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002192861,"about_ca_system_score_gemma":0.0002796289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004251756,"about_ca_topic_score_gemma":0.00001217482,"domain_scores_codex":[0.9950662,0.0004324268,0.0003684493,0.0006226533,0.002861667,0.0006485647],"domain_scores_gemma":[0.9919075,0.001092993,0.00008462311,0.0008258056,0.005957945,0.0001311029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007697836,0.001521393,0.00832418,0.0003598361,0.0001988289,0.0003199574,0.009234096,0.006434443,0.4773389,0.1553842,0.04732879,0.2927856],"study_design_scores_gemma":[0.002189972,0.001157096,0.1157937,0.003945971,0.000006047844,0.0001730588,0.001889975,0.1588039,0.5662294,0.02099839,0.1278679,0.0009445854],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8038504,0.00008353942,0.08904797,0.006391453,0.001454218,0.0003345369,0.000003007871,0.000172921,0.09866192],"genre_scores_gemma":[0.993246,0.00001556507,0.003557034,0.00005816616,0.0007924538,0.000008886328,0.00000298121,0.00002062082,0.002298253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.291841,"threshold_uncertainty_score":0.4768968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232688237901296,"score_gpt":0.429389294090382,"score_spread":0.3061204703002524,"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."}}