{"id":"W3109472813","doi":"10.3390/w12123322","title":"Automated Mapping of Water Table for Cranberry Subirrigation Management: Comparison of Three Spatial Interpolation Methods","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse distance weighting; Kriging; Multivariate interpolation; Interpolation (computer graphics); Statistics; Mathematics; Weighting; Hydrology (agriculture); Environmental science; Sampling (signal processing); Soil science; Agricultural engineering; Computer science; Engineering; Bilinear interpolation; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0008049629,0.0004297974,0.0004094876,0.001542893,0.0003484651,0.0005794929,0.0006588117,0.0002824261,0.0006741606],"category_scores_gemma":[0.001710745,0.0001665312,0.000392367,0.001489169,0.0001863656,0.0003506807,0.0004015495,0.0002100979,0.0001874983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008235396,"about_ca_system_score_gemma":0.001319043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1248348,"about_ca_topic_score_gemma":0.2218196,"domain_scores_codex":[0.9995708,0.0001095966,0.00002056732,0.0001195866,0.0001417591,0.00003760348],"domain_scores_gemma":[0.9993472,0.0002165445,0.00009055398,0.0001078962,0.0002108574,0.00002699857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004458551,0.0001939644,0.1190353,0.0003394717,0.0002600655,0.00008450839,0.0006570956,0.1972618,0.01660657,0.0008630764,0.001306133,0.662946],"study_design_scores_gemma":[0.00003423895,0.00007113833,0.1150756,0.00003275003,0.00005578091,0.0000561571,0.0003172081,0.8751718,0.00657441,0.0002858998,0.002269475,0.00005553086],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7843816,0.0006971855,0.2065698,0.0001383619,0.00002553513,0.0001071012,0.001534565,0.002586542,0.00395934],"genre_scores_gemma":[0.8198926,0.0002393915,0.1775886,0.00001786801,0.000006447718,0.00006452716,0.001237661,0.00007901841,0.0008737816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1248348,"threshold_uncertainty_score":0.2482163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0698416428116969,"score_gpt":0.3108600558617646,"score_spread":0.2410184130500677,"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."}}