{"id":"W2091032015","doi":"10.1139/f03-025","title":"Predicting stream temperatures: geostatistical model comparison using alternative distance metrics","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New York State Department of Environmental Conservation","keywords":"Watershed; Environmental science; Metric (unit); Hydrology (agriculture); Habitat; Trout; STREAMS; Ecology; Computer science; Fishery; Fish <Actinopterygii>; Geology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.01284792,0.00137284,0.001459278,0.003677462,0.0005568813,0.001436321,0.001457331,0.001088337,0.0007505453],"category_scores_gemma":[0.02402409,0.0004942785,0.00167907,0.002369857,0.0005738148,0.001599192,0.001286688,0.001257905,0.000158542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001916605,"about_ca_system_score_gemma":0.002465423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03566691,"about_ca_topic_score_gemma":0.01441776,"domain_scores_codex":[0.9959601,0.002857555,0.0003104556,0.0003889429,0.000307945,0.0001750239],"domain_scores_gemma":[0.979596,0.01756763,0.0006903564,0.0004796983,0.001398226,0.0002680346],"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":[0.0009114281,0.0002110528,0.02425535,0.00005709035,0.0003888569,0.00002256984,0.000068169,0.9372998,0.0002380946,0.0008003894,0.0004229699,0.03532425],"study_design_scores_gemma":[0.00002750574,0.0001159294,0.00117694,0.000002842531,0.00001794463,0.000005958669,0.00001997322,0.9981945,0.00009165013,0.000305941,0.00003273457,0.000008048527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8334829,0.0004495661,0.1629821,0.0004500929,0.00009198114,0.0002966716,0.0006558889,0.0009068555,0.0006838111],"genre_scores_gemma":[0.943949,0.0001861732,0.05409088,0.00005697061,0.00003760496,0.0002973488,0.001044019,0.0000594811,0.0002785263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03566691,"threshold_uncertainty_score":0.07091862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03111519370260734,"score_gpt":0.255137933439448,"score_spread":0.2240227397368407,"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."}}