{"id":"W2559984691","doi":"10.2136/vzj2014.07.0081","title":"Laboratory Calibration Procedures of the Hydra Probe Soil Moisture Sensor:Infiltration Wet-Up vs. Dry-Down","year":2014,"lang":"en","type":"article","venue":"Vadose Zone Journal","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Environment Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Infiltration (HVAC); Water content; Calibration; Mean squared error; Moisture; Environmental science; Soil science; Soil water; Remote sensing; Mathematics; Geology; Materials science; Geotechnical engineering; Statistics; Composite material","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":[0.001409832,0.0006726022,0.0003846817,0.0004068222,0.0004514442,0.0004607866,0.0007299059,0.0003822274,0.001302937],"category_scores_gemma":[0.00207641,0.0002834179,0.0002992153,0.000578525,0.0005302191,0.0003938302,0.0004832835,0.0006950482,0.0005478241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005897807,"about_ca_system_score_gemma":0.0007143312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005971249,"about_ca_topic_score_gemma":0.01483565,"domain_scores_codex":[0.9984336,0.0001987949,0.0001073992,0.0003500313,0.0008480074,0.00006217319],"domain_scores_gemma":[0.9991388,0.0001675919,0.0001431253,0.0001288712,0.0003970857,0.00002436289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002703711,0.0001414743,0.007044771,0.0001775797,0.00003515892,0.00003477627,0.0001518623,0.0009173321,0.9672733,0.0002960192,0.0007321488,0.02292519],"study_design_scores_gemma":[0.00005472642,0.0005979329,0.03533767,0.0000225921,0.00005853654,0.0001350136,0.000129257,0.006808765,0.9498214,0.0001785641,0.006796124,0.00005946006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6740005,0.001139385,0.311134,0.0003055841,0.0002646881,0.00225932,0.002220025,0.001587443,0.007089057],"genre_scores_gemma":[0.7290649,0.001012708,0.260495,0.000512251,0.00004546144,0.002683608,0.00171407,0.000227149,0.004244884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005971249,"threshold_uncertainty_score":0.01187301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004554608255247884,"score_gpt":0.1933037142763759,"score_spread":0.188749106021128,"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."}}