{"id":"W762815129","doi":"10.1007/s10452-015-9531-6","title":"Using watershed characteristics to inform cost-effective stream temperature monitoring","year":2015,"lang":"en","type":"article","venue":"Aquatic Ecology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada; Simon Fraser University","funders":"","keywords":"STREAMS; Environmental science; Watershed; Habitat; Ecosystem; Hydrology (agriculture); Aquatic ecosystem; Environmental monitoring; River ecosystem; Drainage basin; Environmental resource management; Ecology; Geography; Computer science; Environmental engineering","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":[0.001420571,0.000329548,0.0003854728,0.001671975,0.0001867035,0.001341859,0.0003872523,0.0005121044,0.0009486851],"category_scores_gemma":[0.008414211,0.0002038673,0.000213633,0.002172965,0.0001384862,0.001292942,0.0004367641,0.0002821236,0.0001615979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005707382,"about_ca_system_score_gemma":0.0007415526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01040119,"about_ca_topic_score_gemma":0.04156205,"domain_scores_codex":[0.9992005,0.0003661038,0.00009496305,0.0001419218,0.0001368932,0.0000596222],"domain_scores_gemma":[0.9961564,0.001570838,0.001325255,0.0002243507,0.0005513107,0.000171845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001762146,0.0001447834,0.9326719,0.00005235048,0.0001843104,0.00004484329,0.00007595319,0.008487584,0.002532385,0.0003208838,0.0009451181,0.05436361],"study_design_scores_gemma":[0.00003466555,0.0001972859,0.8914308,0.00003729577,0.0002559611,0.0001125677,0.0004417112,0.1002056,0.003014393,0.002067293,0.002172033,0.00003043513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781981,0.0003164936,0.01656362,0.0003996919,0.00002989353,0.00006628204,0.001644192,0.0001077817,0.002674054],"genre_scores_gemma":[0.9903951,0.00011682,0.008564061,0.0000375153,0.00001520053,0.00002889016,0.0005774513,0.0000117374,0.000253303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01040119,"threshold_uncertainty_score":0.02068132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419578808537619,"score_gpt":0.2838423161655509,"score_spread":0.2496465280801748,"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."}}