{"id":"W3160160017","doi":"10.3390/f12050615","title":"Theoretical Development of Plant Root Diameter Estimation Based on GprMax Data and Neural Network Modelling","year":2021,"lang":"en","type":"article","venue":"Forests","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Root (linguistics); Ground-penetrating radar; Dielectric; Root mean square; Biological system; Square root; Mean squared error; Range (aeronautics); Residual; Mathematics; Soil science; Radar; Statistics; Materials science; Environmental science; Computer science; Algorithm; Geometry; Physics; Artificial intelligence; Composite material","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":[0.0007273847,0.0007027448,0.0004754807,0.0008313341,0.00022644,0.0006687033,0.001086471,0.0009713775,0.001176923],"category_scores_gemma":[0.002536889,0.0005552899,0.0005706762,0.0007911767,0.0005229707,0.001426083,0.0005069168,0.001049169,0.0003936185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007868707,"about_ca_system_score_gemma":0.0005656346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008224662,"about_ca_topic_score_gemma":0.003857003,"domain_scores_codex":[0.9996477,0.00006426987,0.0000216884,0.0001375723,0.0001023045,0.00002646065],"domain_scores_gemma":[0.9993508,0.0003296441,0.0000640191,0.0000331138,0.0002084568,0.00001389653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004619537,0.00004287263,0.003135691,0.000270858,0.00005095302,0.000219292,0.0001117148,0.8907927,0.005550634,0.01873981,0.0008860592,0.08015327],"study_design_scores_gemma":[0.000001249155,0.000008505675,0.0004885584,0.00001073449,0.000005454139,0.00002152066,0.000005221328,0.9959638,0.0005347456,0.002555815,0.0003964088,0.000007934499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01186788,0.0007120438,0.9837322,0.0002282584,0.00003266673,0.00002429121,0.00009797286,0.0002551609,0.003049586],"genre_scores_gemma":[0.7550543,0.004320443,0.2319889,0.0002220597,0.0001843419,0.0003590816,0.0005108443,0.0001047037,0.007255259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008224662,"threshold_uncertainty_score":0.01635361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03057620631511106,"score_gpt":0.263044770425965,"score_spread":0.2324685641108539,"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."}}