{"id":"W4288402799","doi":"10.5281/zenodo.2614925","title":"Global Mobile Crushers and Screeners Market Size, Share, Trend, Demand, Opportunity During 2018-2023","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Market size; Business; Economics; Commerce","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004118043,0.0002680677,0.0001608869,0.002254159,0.0004355949,0.001416856,0.0002913364,0.0002955181,0.01351878],"category_scores_gemma":[0.002051411,0.000104299,0.000261346,0.003741116,0.000236364,0.001698485,0.000663274,0.0006014506,0.004986673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009344923,"about_ca_system_score_gemma":0.0006544901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03846345,"about_ca_topic_score_gemma":0.05540741,"domain_scores_codex":[0.9997378,0.00001286443,0.0000131079,0.00004422032,0.000124672,0.00006728891],"domain_scores_gemma":[0.9983233,0.0002291209,0.0004446523,0.00005325999,0.0007025913,0.0002471088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005408857,0.0001917911,0.4435135,0.0006706164,0.00004534723,0.0008070819,0.004541085,0.0006379513,0.001936004,0.00547866,0.4058248,0.1358123],"study_design_scores_gemma":[0.00001501854,0.0001089573,0.8515453,0.0001394072,0.00002126274,0.0003158213,0.005830704,0.0008432292,0.0006909904,0.0003277807,0.1401298,0.00003159796],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6844967,0.001343753,0.0004481751,0.002253445,0.0001665508,0.0001724527,0.2241439,0.0003793356,0.08659569],"genre_scores_gemma":[0.8163198,0.001828814,0.0008290174,0.0007270859,0.0002532527,0.0002865955,0.12723,0.0001927349,0.05233279],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03846345,"threshold_uncertainty_score":0.07647914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373515558392393,"score_gpt":0.235833375339342,"score_spread":0.202098219755418,"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."}}