{"id":"W2508126190","doi":"","title":"Fingerprinting Sources of Suspended Sediment in a Canadian Agricultural Watershed Using the MixSIAR Bayesian Unmixing Model","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Watershed; Sediment; Environmental science; Bayesian probability; Agriculture; Hydrology (agriculture); Geography; Water resource management; Computer science; Geology; Artificial intelligence; Machine learning; Archaeology; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001199819,0.0001517599,0.0001738156,0.00006034676,0.0001577305,0.00005698392,0.0002899595,0.00005791015,0.00001244743],"category_scores_gemma":[0.0001894741,0.0001083602,0.0000389669,0.0002137922,0.00007595604,0.0001208818,0.0002159389,0.0001495495,0.00001089546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004270493,"about_ca_system_score_gemma":0.00007568842,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6880385,"about_ca_topic_score_gemma":0.5149937,"domain_scores_codex":[0.9984288,0.00008077749,0.0003279339,0.0002528549,0.0003462216,0.0005633907],"domain_scores_gemma":[0.9993704,0.00005641691,0.0001501205,0.0001658366,0.00002418221,0.000233034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008713603,0.00002527009,0.4237629,0.00001947293,0.00001787113,0.00001943412,0.0175883,0.5470059,0.009745603,0.0002225441,0.0006992401,0.0008847662],"study_design_scores_gemma":[0.0004484413,0.00002075776,0.0187112,0.0001420362,0.00002428716,0.00001398836,0.00830887,0.968703,0.002078671,0.0008457734,0.0003746646,0.000328269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873868,0.00004810626,0.0008161126,0.0003868298,0.00008490911,0.0001868345,0.000003213204,0.00001555485,0.01107168],"genre_scores_gemma":[0.9867911,0.000001544979,0.01296415,0.00009924474,0.00003678061,0.00000540215,0.000004383503,0.00001304064,0.00008437954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4216971,"threshold_uncertainty_score":0.4938564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02277025927951644,"score_gpt":0.2384073815018337,"score_spread":0.2156371222223173,"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."}}