{"id":"W7007995888","doi":"","title":"Application of proximal soil sensing for environmental characterization of agricultural land","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada; Universiti Putra Malaysia","keywords":"Agricultural land; Land use; Agriculture; Soil water; Characterization (materials science); Hydrology (agriculture)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002187268,0.0003619665,0.000240326,0.001042961,0.0002388633,0.0006127493,0.0002646925,0.000264388,0.001742112],"category_scores_gemma":[0.0005689264,0.0001501117,0.0002183245,0.001049815,0.0001346006,0.0003117037,0.0006484669,0.000200911,0.0006141854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170046,"about_ca_system_score_gemma":0.000347646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004202515,"about_ca_topic_score_gemma":0.006288296,"domain_scores_codex":[0.9998098,0.00002882683,0.000004786817,0.00006731174,0.00005939039,0.00002996795],"domain_scores_gemma":[0.9998792,0.00005349415,0.00001073964,0.00001086918,0.00003525619,0.0000103444],"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":[0.000431238,0.0001554236,0.04111051,0.0003561168,0.00009842929,0.0001256603,0.0003990175,0.04476174,0.2255764,0.001649957,0.002073577,0.6832619],"study_design_scores_gemma":[0.00004773148,0.0004935822,0.2318192,0.0001015317,0.0001972635,0.000350439,0.0008842354,0.5931709,0.1423207,0.007097321,0.0234139,0.0001034188],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7346476,0.002242667,0.2493667,0.0002436053,0.0001004201,0.00009483581,0.002410667,0.0008006822,0.0100929],"genre_scores_gemma":[0.8999263,0.001039464,0.09414256,0.00004287732,0.00004088224,0.00004464544,0.001083421,0.00006326785,0.003616656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004202515,"threshold_uncertainty_score":0.008356094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006574395755574055,"score_gpt":0.2007083919602275,"score_spread":0.1941339962046534,"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."}}