{"id":"W7045505600","doi":"","title":"ARCHIVEIT-3490-DAILY-KBGYBI-20130114054729-00000-crawling113.us.archive.org-6683.warc.gz","year":2013,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001212524,0.002631937,0.001514209,0.007865807,0.002223463,0.006635255,0.003070495,0.001690269,0.8061486],"category_scores_gemma":[0.01028435,0.002111715,0.001041171,0.0106252,0.0007215759,0.004445331,0.004286401,0.001585191,0.9139636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002604955,"about_ca_system_score_gemma":0.002531094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05706628,"about_ca_topic_score_gemma":0.05967003,"domain_scores_codex":[0.998943,0.00009120441,0.00006874275,0.0002176953,0.0005076287,0.0001716653],"domain_scores_gemma":[0.9927859,0.0008840681,0.0002410518,0.002300901,0.00290864,0.0008793711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002616869,0.00001150304,0.0001316616,0.00008990627,0.000003841684,0.000007100457,0.00003894734,0.00003483348,0.00009736755,0.0003450048,0.9864806,0.01273307],"study_design_scores_gemma":[0.00002982143,0.000006909171,0.0009630694,0.00007063025,0.00000448753,0.00002784576,0.00004406527,0.0001448073,0.0006709396,0.0004182978,0.9975949,0.00002422117],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0005161757,0.0002376799,0.003671001,0.0004442089,0.0002409941,0.0001850298,0.5586694,0.1705703,0.2654652],"genre_scores_gemma":[0.002685745,0.0004589481,0.005467081,0.0003750746,0.0001129315,0.0002558897,0.5209929,0.1287561,0.3408952],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1938514,"threshold_uncertainty_score":0.2765055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006538451136839461,"score_gpt":0.1886924563842881,"score_spread":0.1821540052474487,"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."}}