{"id":"W2110769268","doi":"10.1016/j.ecolmodel.2005.01.049","title":"Constrained ordination analysis with flexible response functions","year":2005,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Ordination; Exploit; Gaussian; Perspective (graphical); Computer science; Canonical correlation; Multivariate statistics; Point (geometry); Mathematical optimization; Canonical correspondence analysis; Gaussian network model; Mathematics; Algorithm; Applied mathematics; Artificial intelligence; Machine learning; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003621446,0.001199804,0.001897773,0.001058881,0.0008023003,0.001385701,0.002871568,0.001445834,0.003399109],"category_scores_gemma":[0.0163745,0.001209053,0.002330781,0.001731412,0.0008698186,0.001468301,0.002284744,0.001731601,0.0006749787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006020416,"about_ca_system_score_gemma":0.001114156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007691334,"about_ca_topic_score_gemma":0.00676387,"domain_scores_codex":[0.9974446,0.00184807,0.0001051295,0.0003220803,0.0001744836,0.00010563],"domain_scores_gemma":[0.9910471,0.006655085,0.0003817648,0.001254018,0.0004490445,0.0002129942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003467162,0.00006167044,0.0008937545,0.0001056595,0.0002691531,0.00007416159,0.0001118526,0.9621005,0.00207621,0.009472177,0.0006854085,0.02380273],"study_design_scores_gemma":[0.00001907562,0.00001102828,0.0002081456,0.000002971762,0.00001097407,0.000009574723,0.000005120241,0.9932586,0.0002073578,0.006070335,0.0001865406,0.00001031587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01931853,0.00006100707,0.9798697,0.00004223222,0.00001138532,0.0000234417,0.0001247335,0.0003637981,0.000185151],"genre_scores_gemma":[0.4869177,0.0001114281,0.5084214,0.0000967954,0.00003362116,0.0004432937,0.001073632,0.001021459,0.00188086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007691334,"threshold_uncertainty_score":0.01915228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05993156937348727,"score_gpt":0.2154925227811975,"score_spread":0.1555609534077102,"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."}}