{"id":"W2186716202","doi":"","title":"Restoring Streams with Large Restoring Streams with Large Restoring Streams with Large Restoring Streams with Large Restoring Streams with Large W W W W Wood: a Synthesis ood: a Synthesis ood: a Synthesis ood: a Synthesis ood: a Synthesis","year":2003,"lang":"en","type":"article","venue":"","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"STREAMS; Stream restoration; Environmental science; Logging; Floodplain; Hydrology (agriculture); Geography; Engineering; Forestry; Computer science; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001375314,0.0004738803,0.0003505711,0.0009807509,0.0008122373,0.00137926,0.0006142635,0.0005110316,0.008216005],"category_scores_gemma":[0.00228851,0.0002174793,0.0005090519,0.0009277101,0.0005003912,0.001319386,0.0008192207,0.000337723,0.0006437399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001579905,"about_ca_system_score_gemma":0.00280911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004731853,"about_ca_topic_score_gemma":0.01920849,"domain_scores_codex":[0.9993232,0.0001673202,0.00007908734,0.00009178204,0.0002857308,0.00005274476],"domain_scores_gemma":[0.9990018,0.0003372195,0.0001468559,0.0001163092,0.0003212532,0.00007658596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000404219,0.0004471114,0.0108495,0.005409324,0.0001459711,0.001540696,0.001458691,0.08502974,0.02370392,0.02890816,0.01804824,0.8240545],"study_design_scores_gemma":[0.0004927759,0.002369922,0.03483851,0.002247317,0.0008588731,0.002743909,0.007429766,0.07357596,0.09449746,0.04199262,0.7387457,0.0002072322],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.4530126,0.006825727,0.3619885,0.002660643,0.0008093172,0.004642455,0.003181804,0.002828345,0.1640506],"genre_scores_gemma":[0.7078203,0.004687244,0.2604576,0.0003322845,0.00009636098,0.0009995749,0.001227039,0.0001864079,0.02419318],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.008216005,"threshold_uncertainty_score":0.02748525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009283266169953841,"score_gpt":0.208007899031214,"score_spread":0.1987246328612602,"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."}}