{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006029114,0.0001166054,0.0001667673,0.00002218806,0.00004116738,0.000005014122,0.00006540323,0.00009699028,0.0001108164],"category_scores_gemma":[0.00000992087,0.00008451547,0.00005091423,0.00003822433,0.00003621674,0.00006395872,0.00001868158,0.00002912191,0.00001396294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004492891,"about_ca_system_score_gemma":0.000005487893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008435397,"about_ca_topic_score_gemma":0.00007960026,"domain_scores_codex":[0.9992536,0.000008721011,0.0002656929,0.0001962691,0.0001689048,0.0001067537],"domain_scores_gemma":[0.999473,0.000026387,0.0003680634,0.00009754515,0.00001010146,0.000024896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002563064,0.00002574617,0.005035609,0.00007331108,0.00001074288,3.670525e-8,0.0001444815,0.000006661706,0.9382125,0.00005372924,0.00002246504,0.05638909],"study_design_scores_gemma":[0.0002923942,0.00005561883,0.7511629,0.00005341276,0.00004524061,9.755142e-7,0.000142969,0.0007343587,0.2467096,0.0001372296,0.0004856518,0.0001795631],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787796,0.000003926484,0.01563912,0.00001278996,0.0001006033,0.0005570625,0.0002241666,0.000009615588,0.004673093],"genre_scores_gemma":[0.989603,0.00001692599,0.001432476,0.000006521313,0.00004150371,0.00002501236,0.003710539,0.00001406762,0.005149914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7461273,"threshold_uncertainty_score":0.3446441,"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."}}