{"id":"W2041417229","doi":"10.1556/comec.4.2003.1.13","title":"The use of matrix models to detect natural and pollution-induced forest gradients","year":2003,"lang":"en","type":"article","venue":"Community Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Ordination; Quadrat; Gradient analysis; Abundance (ecology); Environmental science; Vegetation (pathology); Natural (archaeology); Ecology; Animal ecology; Markov chain; Pollution; Geography; Physical geography; Mathematics; Biology; Statistics; Transect","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":[0.0003282648,0.00006666432,0.0001035867,0.00002373028,0.000872541,0.000008108213,0.0001663059,0.00005513138,0.00001757696],"category_scores_gemma":[0.0003104151,0.00005152108,0.00002074393,0.0001043596,0.0002887107,0.00009536772,0.0002497289,0.0002203982,0.00001860922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005924485,"about_ca_system_score_gemma":0.000006245812,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003863047,"about_ca_topic_score_gemma":0.07057093,"domain_scores_codex":[0.9991265,0.0004556307,0.0001293635,0.00007275007,0.00004260771,0.0001731678],"domain_scores_gemma":[0.999131,0.0005372093,0.00005357717,0.0002285412,0.0000131188,0.00003657106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007205244,0.0001592118,0.9289482,0.00001053998,0.00008747928,0.000002060339,0.004217637,0.03248298,0.001703135,0.02850856,0.001231295,0.002576834],"study_design_scores_gemma":[0.0002376773,0.0001818058,0.9701465,0.000001745892,0.00001123124,0.000008578583,0.0003535472,0.005423667,0.00006044499,0.02268805,0.000806689,0.00008004849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984681,0.00003410619,0.0002090866,0.0003204565,0.0001558649,0.0001611338,0.000002223634,0.0000090945,0.0006399142],"genre_scores_gemma":[0.9984662,0.00003509831,0.0009663663,0.0002727464,0.000001452368,0.00001708862,0.000001149133,0.000003718565,0.0002361755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07018463,"threshold_uncertainty_score":0.9463887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04481663175969219,"score_gpt":0.2665666556864234,"score_spread":0.2217500239267312,"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."}}