{"id":"W2594411372","doi":"","title":"Wireless Sensor Networks: Some Insights Gained in West African Hydrology","year":2015,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wireless sensor network; Hydrology (agriculture); Computer science; Environmental science; Remote sensing; Geography; Geology; Computer network","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":[0.0008533139,0.0004147553,0.0002315212,0.0009314996,0.0006265004,0.001879834,0.0003764665,0.0007184073,0.001063035],"category_scores_gemma":[0.002498826,0.0002558047,0.0001603361,0.002415997,0.001844491,0.004008075,0.000720734,0.0008693479,0.0001263566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363468,"about_ca_system_score_gemma":0.0005464632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009503893,"about_ca_topic_score_gemma":0.01233948,"domain_scores_codex":[0.9998405,0.00005507291,0.000011228,0.00002749836,0.00003765225,0.00002802414],"domain_scores_gemma":[0.9992968,0.0004654739,0.00007649767,0.00003132221,0.0001069129,0.00002310634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001431513,0.00007322846,0.02131803,0.0009483786,0.00007777041,0.002498807,0.005830926,0.08649287,0.006617363,0.6446546,0.004708477,0.2266364],"study_design_scores_gemma":[0.00001485059,0.0001339614,0.03275681,0.000712086,0.0000787524,0.001399753,0.01494949,0.1554658,0.006981655,0.5784481,0.2089635,0.00009528276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5299342,0.1465137,0.1686105,0.03639178,0.0008068933,0.00009359948,0.0006013726,0.000105393,0.1169426],"genre_scores_gemma":[0.9033337,0.0724128,0.01543554,0.0004561238,0.0003987435,0.0000199817,0.00006987554,0.00002200579,0.007851173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009503893,"threshold_uncertainty_score":0.01889718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188151285940017,"score_gpt":0.227638775991845,"score_spread":0.2088236473978433,"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."}}