{"id":"W6968844168","doi":"10.5281/zenodo.6313573","title":"Predictive Soil Mapping of Historical Soil Properties using Multi-Temporal Remote Sensing Imagery and Terrain Data","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Terrain; Digital soil mapping; Satellite imagery; Vegetation (pathology); Thematic Mapper; Reflectivity; Digital elevation model","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0004750249,0.0004450368,0.0002350332,0.001609799,0.0001759896,0.0006329764,0.0006288461,0.0003469829,0.005491006],"category_scores_gemma":[0.001495046,0.0002364573,0.000522803,0.002264319,0.0001178135,0.00068792,0.0003392817,0.000347657,0.001047715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003728989,"about_ca_system_score_gemma":0.0004966673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02108448,"about_ca_topic_score_gemma":0.02717417,"domain_scores_codex":[0.9998779,0.00002075545,0.000007113856,0.00005013022,0.00002734671,0.00001673236],"domain_scores_gemma":[0.9995638,0.0001782153,0.00006075027,0.00007130831,0.00009239675,0.00003355083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003006848,0.0002445662,0.06989757,0.000300603,0.0002690824,0.0002942911,0.00008433013,0.7173646,0.006860116,0.00174833,0.01690886,0.185727],"study_design_scores_gemma":[0.00003376281,0.00002069913,0.03001703,0.00002757205,0.00003783965,0.00004427145,0.00006047929,0.9640356,0.001583961,0.001475723,0.002642835,0.00002024601],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.748621,0.0010171,0.1578299,0.0008971756,0.0002432834,0.0001219599,0.07532207,0.008054304,0.007893135],"genre_scores_gemma":[0.9243076,0.0002708022,0.0512082,0.00003310058,0.00005232178,0.00006274345,0.02219076,0.0002392907,0.001635182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02108448,"threshold_uncertainty_score":0.04192352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08869299808175385,"score_gpt":0.2456547769408432,"score_spread":0.1569617788590894,"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."}}