{"id":"W2073786579","doi":"10.2136/sssaj2004.0317","title":"Use of Spectral Analysis to Detect Changes in Spatial Variability of Forest Floor Properties","year":2006,"lang":"en","type":"article","venue":"Soil Science Society of America Journal","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Ministère des Ressources naturelles et des Forêts (Québec)","funders":"","keywords":"Environmental science; Transect; Spatial ecology; Spatial variability; Forest floor; Microclimate; Common spatial pattern; Spatial heterogeneity; Spatial distribution; Forest plot; Soil science; Atmospheric sciences; Hydrology (agriculture); Ecology; Soil water; Geology; Remote sensing; Mathematics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001085892,0.00009907492,0.0002989931,0.0001101816,0.0001199508,0.00002951322,0.0003354985,0.00002825309,0.0001794634],"category_scores_gemma":[0.0002246483,0.00007979405,0.0001694681,0.002149536,0.001657705,0.0002059652,0.000157388,0.0001184208,0.000001825504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001575412,"about_ca_system_score_gemma":0.00009309678,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02919432,"about_ca_topic_score_gemma":0.00421252,"domain_scores_codex":[0.9982834,0.00004830221,0.000400561,0.000230146,0.0007073949,0.0003302321],"domain_scores_gemma":[0.9992496,0.00005827812,0.0003417503,0.000182346,0.00007122757,0.00009677208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002490042,0.0001121574,0.664104,0.00001685735,0.00003131794,8.43856e-7,0.001859401,0.1567448,0.1596589,0.000006671411,0.0002007496,0.01723942],"study_design_scores_gemma":[0.0001147123,0.000182693,0.9411553,0.00001991869,0.00004974842,0.00000253549,0.000487873,0.0392157,0.01809633,0.0004901198,0.00007839814,0.0001066461],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714606,0.000008005707,0.02795952,0.0002366109,0.0000441904,0.00008868577,0.00001111171,0.000003407922,0.00018782],"genre_scores_gemma":[0.958986,0.00003006645,0.04088172,0.0000554535,0.00002108436,0.000002125937,4.851548e-7,0.000003510399,0.00001949809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2770513,"threshold_uncertainty_score":0.9772704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514700240824386,"score_gpt":0.225293222021906,"score_spread":0.2101462196136621,"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."}}