{"id":"W1989505837","doi":"10.1002/esp.1888","title":"Quantifying the temporal dynamics of wood in large rivers: field trials of wood surveying, dating, tracking, and monitoring techniques","year":2009,"lang":"en","type":"article","venue":"Earth Surface Processes and Landforms","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Resources Conservation Service","keywords":"Environmental science; Temporal resolution; Flood myth; Range (aeronautics); Hydrology (agriculture); Video monitoring; Remote sensing; Vegetation (pathology); Physical geography; Computer science; Geology; Geography; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001619688,0.0001336068,0.0003528662,0.00003287396,0.0001057858,0.00002205574,0.0001395535,0.0001023684,0.0001055461],"category_scores_gemma":[0.0001976111,0.00008360853,0.00003007308,0.0002292289,0.00009572847,0.0003472217,0.00003616284,0.0001616462,4.287966e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000509934,"about_ca_system_score_gemma":0.00001813488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004725514,"about_ca_topic_score_gemma":0.002278011,"domain_scores_codex":[0.9989242,0.00004988296,0.0004416209,0.0002063036,0.0001660071,0.0002119702],"domain_scores_gemma":[0.9993533,0.0002291664,0.0002563127,0.0001060306,0.00001877687,0.00003635591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006845643,0.00007767163,0.9879599,0.0001628899,0.00001038681,0.000002132312,0.001320982,0.00008740567,0.0007328854,0.00003113956,0.000004416563,0.009541702],"study_design_scores_gemma":[0.002084796,0.001246652,0.7313932,0.0007536752,0.00008563884,0.00001309444,0.002384741,0.001561945,0.2569405,0.001731097,0.001230655,0.0005740046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976232,0.0009764312,0.0005956167,0.0001899146,0.00002310292,0.0002069023,0.00001745168,0.00002173445,0.00034562],"genre_scores_gemma":[0.9981385,0.0008459846,0.0009220655,0.00003154669,0.00001062171,0.000002698745,0.00001122527,0.000005371982,0.00003192154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2565668,"threshold_uncertainty_score":0.3409457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03222637198920877,"score_gpt":0.2926024532820474,"score_spread":0.2603760812928386,"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."}}