{"id":"W2378239425","doi":"10.1371/journal.pone.0154269","title":"The Surales, Self-Organized Earth-Mound Landscapes Made by Earthworms in a Seasonal Tropical Wetland","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Muséum National d'Histoire Naturelle; Institut Universitaire de France; Universidad Tecnológica de Pereira; Centre National d’Etudes Spatiales; Fonds National de la Recherche Luxembourg; Centre National de la Recherche Scientifique; European Commission; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Wetland; Ecology; Chronosequence; Ecosystem engineer; Earthworm; Vegetation (pathology); Arid; Landform; Ecosystem; Digging; Biogeochemistry; Geology; Environmental science; Geography; Biology; Paleontology","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":[0.0001646131,0.0001068652,0.0001466434,0.00001158141,0.0001371424,0.00006542778,0.0002606243,0.00006295645,0.0003326987],"category_scores_gemma":[0.0000445761,0.0000525792,0.00002875695,0.0001300584,0.00008584958,0.0001506223,0.0001102569,0.0001007671,0.0004859991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006367029,"about_ca_system_score_gemma":0.00001123727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005557569,"about_ca_topic_score_gemma":0.0030283,"domain_scores_codex":[0.9987984,0.0000702102,0.000174756,0.0002446224,0.0003880966,0.0003239675],"domain_scores_gemma":[0.9994792,0.0001771456,0.0000457521,0.0001999294,0.000006103442,0.00009180947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002994679,0.0003324214,0.8416054,0.000009238453,0.0000241355,0.000006564626,0.00007124156,0.000001633494,0.1570611,0.0001095068,0.0003465767,0.0004022311],"study_design_scores_gemma":[0.001833263,0.0001556913,0.9731939,0.0001834357,0.00002691167,0.00001117505,0.0000587264,0.00359955,0.01576971,0.0003685002,0.004436668,0.0003624236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965982,0.0001383981,0.00002515123,0.001886223,0.00002160734,0.000181875,0.00001625582,0.00003411134,0.001098138],"genre_scores_gemma":[0.9943645,0.0003341359,0.0002258644,0.00003859586,0.00003347937,0.00001549598,0.000002189953,0.00001128632,0.00497445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1412914,"threshold_uncertainty_score":0.6246698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005699686073792835,"score_gpt":0.1667514477340559,"score_spread":0.1610517616602631,"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."}}