{"id":"W3093832362","doi":"","title":"Antecedent moisture conditions and catchment morphology as controls on spatial patterns of runoff generation in small forest catchments","year":2009,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Antecedent moisture; Surface runoff; Hydrology (agriculture); Environmental science; Drainage basin; Storm; Ephemeral key; Water balance; Streamflow; Runoff curve number; Geology; Geography; Ecology","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":[0.0001873307,0.0002225891,0.0002814421,0.00006384277,0.0001185729,0.00002774843,0.0001623008,0.0001207727,0.0001392977],"category_scores_gemma":[0.00002014941,0.0001991839,0.00003853839,0.00005409611,0.00009760156,0.0001209098,0.00008721332,0.000163144,0.00006755068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000813644,"about_ca_system_score_gemma":0.00001294604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004731108,"about_ca_topic_score_gemma":0.01133162,"domain_scores_codex":[0.9986047,0.00007118162,0.0003804065,0.000419443,0.0001769341,0.0003473216],"domain_scores_gemma":[0.999501,0.00002670723,0.0001713355,0.0001986046,0.00001612622,0.00008617932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001561589,0.0008661766,0.6730376,0.0000150817,0.00007740739,0.0001765054,0.001037164,0.06274395,0.2541231,0.00218254,0.001211287,0.004373034],"study_design_scores_gemma":[0.0009668717,0.0005378376,0.9785486,0.00001875152,0.00002387352,0.000006475847,0.00005824923,0.0007813498,0.01737788,0.001340971,0.0001455856,0.0001935839],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939759,0.00001462634,0.0001461874,0.002711008,0.0001691543,0.0004282333,0.00001856743,0.00001701476,0.0025193],"genre_scores_gemma":[0.9978289,0.00009056474,0.0000911629,0.001275466,0.00006183749,0.00004407901,0.0001094573,0.000006546899,0.0004919597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3055109,"threshold_uncertainty_score":0.8122483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847542974104543,"score_gpt":0.2546278031576094,"score_spread":0.2361523734165639,"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."}}