{"id":"W4391882755","doi":"10.22230/jwsm.2024v7n1a53","title":"Fitting Power-Law Relations in Watershed Science and Analysis, with an Example Using the R Language","year":2024,"lang":"en","type":"article","venue":"Confluence Journal of Watershed Science and Management","topic":"Pesticide and Herbicide Environmental Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Watershed; Power (physics); Power law; Law; Sociology; Mathematics; Environmental science; Linguistics; Computer science; Law and economics; Political science; Philosophy; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.02731569,0.002438634,0.001074677,0.003301114,0.0008582429,0.003165111,0.002540519,0.002022863,0.01227772],"category_scores_gemma":[0.08997139,0.0009750479,0.002025474,0.007537518,0.002713376,0.004251816,0.002516695,0.003559501,0.0121782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143054,"about_ca_system_score_gemma":0.002102321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005854891,"about_ca_topic_score_gemma":0.007622238,"domain_scores_codex":[0.9799008,0.01315768,0.001514047,0.002010101,0.003188324,0.0002291735],"domain_scores_gemma":[0.93149,0.05600343,0.003948256,0.005120891,0.003225455,0.0002120224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002041477,0.000224793,0.01114991,0.003243425,0.0006576595,0.001372237,0.002402475,0.1185092,0.007419303,0.3424181,0.1121288,0.40027],"study_design_scores_gemma":[0.0000876877,0.0002493494,0.005467459,0.0008015973,0.0001428708,0.001388173,0.0004980774,0.2718752,0.009385599,0.3992834,0.3105398,0.0002808935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001323981,0.0006221303,0.9886882,0.0008487962,0.00007638006,0.0001025423,0.0005992088,0.004771957,0.002966801],"genre_scores_gemma":[0.03209361,0.001541745,0.9574599,0.0006461286,0.0001203007,0.0005040902,0.000965853,0.003852744,0.002815674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02731569,"threshold_uncertainty_score":0.1444609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193241222492728,"score_gpt":0.2590258692430433,"score_spread":0.2397017469937705,"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."}}