{"id":"W4306292507","doi":"10.31223/x5m636","title":"Harnessing hyperspectral imagery to map surface water presence and hyporheic flow properties of headwater stream networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Riparian zone; Channel (broadcasting); Remote sensing; Environmental science; Hydrology (agriculture); Satellite imagery; Subsurface flow; Geology; Computer science; Habitat; Groundwater","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":[0.0005344187,0.0005232979,0.00017302,0.0006880171,0.000170359,0.0004710143,0.0004697612,0.0003631033,0.0005034013],"category_scores_gemma":[0.0008830887,0.0002198959,0.0003495577,0.0003408557,0.0002610389,0.0007405198,0.0002942215,0.0003942513,0.000262535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003076076,"about_ca_system_score_gemma":0.0002808041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0202074,"about_ca_topic_score_gemma":0.02762255,"domain_scores_codex":[0.9998623,0.00002313848,0.000004996641,0.00005760181,0.0000294222,0.00002250105],"domain_scores_gemma":[0.9997553,0.00007259839,0.00003620517,0.00005645891,0.00005594224,0.00002352173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001825065,0.0007813132,0.09148587,0.00003888101,0.000198236,0.0001068406,0.0001316693,0.6393131,0.02761974,0.0003460321,0.002769344,0.2370265],"study_design_scores_gemma":[0.000006222666,0.0000285405,0.01834855,0.000003013315,0.000009391789,0.00001244716,0.0000234845,0.9786527,0.002543505,0.0001722061,0.0001935532,0.000006470889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975253,0.00007386412,0.02133355,0.0001086606,0.00001751139,0.00004641133,0.000572652,0.0009985932,0.001595713],"genre_scores_gemma":[0.9802431,0.00005213285,0.0173765,0.00004312372,0.00001581469,0.00001837201,0.001580247,0.00002540544,0.0006452339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0202074,"threshold_uncertainty_score":0.04017955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668871877803941,"score_gpt":0.2161449513263576,"score_spread":0.1994562325483182,"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."}}