{"id":"W6922688072","doi":"10.1371/journal.pgen.1010410.s017","title":"Supplementary information.","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Graph; Exploit; Divergence (linguistics); Heuristic; Markov chain Monte Carlo; Sampling (signal processing)","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":[0.002190483,0.001369688,0.001423909,0.003423776,0.001674745,0.003166071,0.003157821,0.002390313,0.816812],"category_scores_gemma":[0.01354898,0.000930316,0.0008612908,0.005201614,0.0004284246,0.002628909,0.002428672,0.001573888,0.5386597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001393626,"about_ca_system_score_gemma":0.002994312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008268747,"about_ca_topic_score_gemma":0.01297434,"domain_scores_codex":[0.9987031,0.0002215119,0.0001461707,0.0003442127,0.0004207119,0.0001642092],"domain_scores_gemma":[0.9946449,0.002208062,0.0003100598,0.0008958472,0.001414799,0.0005263903],"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":[0.000133601,0.00005584295,0.001008491,0.0008109963,0.00002445837,0.00007818515,0.00006548282,0.0003943155,0.0003823417,0.00262686,0.9702077,0.0242117],"study_design_scores_gemma":[0.00009822385,0.00003617146,0.001944276,0.0002947352,0.00002491223,0.0001214448,0.00009160709,0.0004503958,0.0004364632,0.00567853,0.9907916,0.00003171649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005722257,0.0003990714,0.003824293,0.0005879157,0.000656846,0.0002023648,0.9513637,0.003645173,0.03874831],"genre_scores_gemma":[0.005142993,0.0007964874,0.01012781,0.001464129,0.0002032272,0.0008622099,0.9264082,0.002737178,0.05225779],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.183188,"threshold_uncertainty_score":0.2612953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04718580131259003,"score_gpt":0.2336663966323235,"score_spread":0.1864805953197335,"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."}}