{"id":"W2075203254","doi":"10.1890/09-0460.1","title":"Multiscale codependence analysis: an integrated approach to analyze relationships across scales","year":2010,"lang":"en","type":"article","venue":"Ecology","topic":"Data Analysis with R","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Ecology; Scale (ratio); Geography; Computer science; Biology; Cartography","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":[0.001463225,0.0001951789,0.0004229512,0.0004765656,0.0003750375,0.000273058,0.002201507,0.0002280127,0.00005466945],"category_scores_gemma":[0.0005032572,0.0001751477,0.0001352471,0.00347049,0.0001434992,0.0008626462,0.0004805963,0.0006556231,0.0005016715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004499362,"about_ca_system_score_gemma":0.00007296449,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002152202,"about_ca_topic_score_gemma":0.04797224,"domain_scores_codex":[0.9974927,0.0003773955,0.0004136678,0.000938029,0.0002617314,0.0005164752],"domain_scores_gemma":[0.997385,0.0002349382,0.0001478521,0.001676252,0.000222982,0.0003330045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002162974,0.0009694975,0.9215673,0.000007624745,0.0008810681,0.00003739981,0.00388325,0.0263446,0.002699164,0.03112532,0.002416162,0.01004704],"study_design_scores_gemma":[0.0001411885,0.00004478086,0.5689176,7.552485e-7,0.00009774119,0.00001413559,0.000200557,0.4286022,0.0001196551,0.000321857,0.001328458,0.0002110156],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4271232,0.000004677839,0.5714875,0.0003632063,0.000196882,0.0001245099,0.00003265367,0.000155557,0.00051182],"genre_scores_gemma":[0.7796758,7.421221e-7,0.2194678,0.0002315745,0.00003991638,0.00004426462,0.0001620202,0.000007866879,0.0003699777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4022577,"threshold_uncertainty_score":0.9693998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587042978143286,"score_gpt":0.299021606548138,"score_spread":0.2731511767667051,"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."}}