{"id":"W1973676953","doi":"10.1890/09-1255.1","title":"Inferential permutation tests for maximum entropy models in ecology","year":2010,"lang":"en","type":"article","venue":"Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principle of maximum entropy; Ecology; Statistical hypothesis testing; Null hypothesis; Mathematics; Permutation (music); Entropy (arrow of time); Inference; Conditional independence; Relative abundance distribution; Null model; Statistics; Econometrics; Computer science; Statistical physics; Abundance (ecology); Relative species abundance; Biology; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002570434,0.00009335302,0.0001559478,0.00006010876,0.0001333086,0.000005886611,0.000123158,0.0001982047,0.00133895],"category_scores_gemma":[0.000194528,0.00009519954,0.00003464643,0.00007529983,0.0002137198,0.0001339462,0.00009796324,0.0001857909,0.0002642272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007618709,"about_ca_system_score_gemma":0.00001958388,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003208236,"about_ca_topic_score_gemma":0.0635642,"domain_scores_codex":[0.9991488,0.00005745372,0.0001988846,0.0002503553,0.00004347102,0.0003010579],"domain_scores_gemma":[0.9994364,0.0003436662,0.00007117182,0.00009960515,0.00001351103,0.00003566975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003354042,0.0001624944,0.9657181,0.000004547911,0.0000115668,0.00000476259,0.0004869639,0.009563155,0.003289156,0.01910144,0.0006152878,0.001008994],"study_design_scores_gemma":[0.0006660608,0.000125439,0.8149954,3.627646e-7,0.000008086217,0.000006050392,0.0000305397,0.05270327,0.00005153363,0.1308181,0.0005032291,0.00009193103],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922341,0.000002533062,0.001825223,0.001129404,0.0008910506,0.0003430858,0.000005425546,0.00002401144,0.003545221],"genre_scores_gemma":[0.9961384,0.000006632787,0.002773866,0.0003947553,0.00004211208,0.0002257858,0.00002218065,0.00000823034,0.0003880356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1507227,"threshold_uncertainty_score":0.9995739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077560332846105,"score_gpt":0.2559755757551193,"score_spread":0.2451999724266582,"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."}}