{"id":"W7161975848","doi":"10.82308/31233","title":"Application of proximal soil sensing for environmental characterization of agricultural land","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cambisol; Multispectral Scanner; Environmental effect; Soil cover","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002848203,0.0005093007,0.0003286698,0.0009277738,0.0002146889,0.000608934,0.0003743703,0.0004669208,0.001320673],"category_scores_gemma":[0.0004085162,0.0001844768,0.0003057501,0.001059094,0.0001388093,0.0005397052,0.0004555805,0.0002422271,0.0005206353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171784,"about_ca_system_score_gemma":0.0004180456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003299211,"about_ca_topic_score_gemma":0.005821626,"domain_scores_codex":[0.9997882,0.00003683631,0.000006239683,0.00007226512,0.00007251982,0.00002399001],"domain_scores_gemma":[0.999873,0.00004621644,0.00001609359,0.00001310003,0.00004221608,0.000009385177],"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":[0.0003780583,0.0001343067,0.02828258,0.0007848378,0.0001207361,0.0001559571,0.0002925191,0.0296475,0.5700951,0.001303043,0.000995063,0.3678102],"study_design_scores_gemma":[0.0000460161,0.0005948225,0.09718186,0.000100308,0.0001995618,0.0004335781,0.0006918651,0.5829437,0.2935463,0.00543366,0.01868169,0.0001465826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5726205,0.00325205,0.4106305,0.0002453088,0.0001442959,0.0001831545,0.002160083,0.001632041,0.009132122],"genre_scores_gemma":[0.8584743,0.00125994,0.1364369,0.00008873677,0.00003760861,0.00008826034,0.0007400622,0.00004853234,0.002825744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003299211,"threshold_uncertainty_score":0.006560028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004146545491721965,"score_gpt":0.198416059499454,"score_spread":0.194269514007732,"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."}}