{"id":"W2792253887","doi":"10.1016/j.ijsrc.2018.01.003","title":"Optimizing the dataset size of a topo-bathymetric survey for Hammam Debagh Dam, Algeria","year":2018,"lang":"en","type":"article","venue":"International Journal of Sediment Research","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Bathymetry; Geology; Sediment; Geostatistics; Hydrology (agriculture); Environmental science; Geomorphology; Oceanography; Statistics; Geotechnical engineering; Spatial variability","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":[0.004271552,0.00007999948,0.0001700749,0.0001048725,0.0001434368,0.00008123612,0.0009997331,0.00005033085,0.0008195132],"category_scores_gemma":[0.0006636689,0.00002753122,0.000140451,0.0006858514,0.0001866653,0.0001319757,0.00009507449,0.000186578,0.00000776145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004970781,"about_ca_system_score_gemma":0.00005086022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006688804,"about_ca_topic_score_gemma":0.0003744742,"domain_scores_codex":[0.9976743,0.0001865842,0.0004464154,0.0001437626,0.001322346,0.0002266022],"domain_scores_gemma":[0.9956135,0.002350749,0.0002588836,0.00006669048,0.001617245,0.00009297554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006158942,0.001931278,0.1191832,0.00002074467,0.002567296,0.00005419562,0.001544997,0.00009691408,0.6489688,0.001669545,0.1286811,0.08912295],"study_design_scores_gemma":[0.001547839,0.002839624,0.7813041,0.00007800576,0.00008207611,0.0000251405,0.0007644712,0.0004945028,0.06844918,0.0006082167,0.1436039,0.000202981],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941929,0.0001558279,0.0001294669,0.003667119,0.0006556143,0.0002146089,0.0007441402,0.000004209403,0.0002361315],"genre_scores_gemma":[0.9982749,0.0000964233,0.0003351582,0.0002163063,0.0006200408,0.000006322993,0.0002638815,0.000001058981,0.0001859577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6621209,"threshold_uncertainty_score":0.8973095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1493263665864882,"score_gpt":0.3979911398518575,"score_spread":0.2486647732653693,"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."}}