{"id":"W4237420363","doi":"10.14322/publons.r2954115","title":"10.14322/publons.r2954115","year":2000,"lang":"en","type":"dataset","venue":"Time to knit","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Copper; Environmental science; Risk assessment; Chemistry; Computer science; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001231741,0.003633669,0.00170504,0.00434465,0.000846982,0.003485449,0.00370483,0.003003068,0.392301],"category_scores_gemma":[0.007403974,0.001530844,0.001522603,0.008330983,0.000621924,0.00199462,0.002878727,0.001376871,0.6108652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001584987,"about_ca_system_score_gemma":0.002170855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0220688,"about_ca_topic_score_gemma":0.03154283,"domain_scores_codex":[0.9988853,0.0001727254,0.0001301617,0.0004064717,0.0002116101,0.0001937014],"domain_scores_gemma":[0.9974474,0.0006466371,0.000263158,0.0007581802,0.0005316145,0.0003530215],"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":[0.00006429138,0.00001547849,0.0004057122,0.0005500702,0.0000268486,0.00001228013,0.00001727186,0.0002891139,0.00008806073,0.000258791,0.9955624,0.002709566],"study_design_scores_gemma":[0.0004010185,0.00003505053,0.00211712,0.0002466919,0.00003801943,0.00003321316,0.00005912412,0.0006547109,0.000449603,0.001141587,0.9947806,0.00004306465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005686189,0.00004774926,0.00008218803,0.00005269191,0.00002480041,0.000007846577,0.9974399,0.001403428,0.0008845557],"genre_scores_gemma":[0.0003103007,0.00005420296,0.0002179475,0.00005963179,0.000009573532,0.00005388752,0.9972866,0.0003995919,0.001608327],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.607699,"threshold_uncertainty_score":0.8668088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005402504922338446,"score_gpt":0.1814875885847471,"score_spread":0.1760850836624087,"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."}}