{"id":"W4403681330","doi":"10.22541/essoar.172970220.06666803/v1","title":"Revisiting k : Time-varying stream litter breakdown rates","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trinity College","funders":"","keywords":"Litter; Environmental science; Biology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001705497,0.0006928876,0.0004793631,0.0006454247,0.0004063419,0.001583056,0.0016609,0.001473413,0.0045405],"category_scores_gemma":[0.02154661,0.0004194713,0.0007898193,0.0009380119,0.000602707,0.003118251,0.0008701767,0.002143213,0.0009961193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000923346,"about_ca_system_score_gemma":0.001117908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01632006,"about_ca_topic_score_gemma":0.01113386,"domain_scores_codex":[0.9990979,0.0001366733,0.00004821641,0.0004065677,0.0001569441,0.000153635],"domain_scores_gemma":[0.9946765,0.003278687,0.0005047044,0.0007848804,0.0005687965,0.0001865144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001193971,0.0003711687,0.07650237,0.0008909987,0.0003394746,0.001134754,0.0006182522,0.6669376,0.02855683,0.03323336,0.02956576,0.1606554],"study_design_scores_gemma":[0.00003481476,0.00004280917,0.01987577,0.00006459511,0.00005604868,0.0002350461,0.0001918115,0.9563472,0.007921722,0.01245598,0.002718254,0.00005588109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6516764,0.001530535,0.3176346,0.003977875,0.001354123,0.00009721557,0.006346383,0.002780933,0.01460201],"genre_scores_gemma":[0.9786507,0.0004042235,0.01637715,0.0002122492,0.0001011366,0.00002787191,0.001043956,0.0004584986,0.002724247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01632006,"threshold_uncertainty_score":0.03245014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432938003425376,"score_gpt":0.2420763815346676,"score_spread":0.2277470015004139,"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."}}