{"id":"W2597532406","doi":"10.1111/ele.12757","title":"How to make more out of community data? A conceptual framework and its implementation as models and software","year":2017,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":1026,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Université de Sherbrooke","funders":"Helsingin Yliopiston Tiedesäätiö; Norges Forskningsråd; Academy of Finland; Helsingin Yliopisto","keywords":"Ecology; Computer science; Inference; Correlative; Community; Conceptual framework; Community structure; Data science; Bayesian probability; Statistical inference; Data mining; Artificial intelligence; Biology; Habitat; Mathematics","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":[0.0002156322,0.00008019895,0.0001358008,0.00001744607,0.000640231,0.00002360074,0.0003043568,0.00006771601,0.00004732145],"category_scores_gemma":[0.000239957,0.00008071253,0.000009322616,0.00001476702,0.0006419891,0.0002554244,0.0009598042,0.0001636461,0.00001158292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002597722,"about_ca_system_score_gemma":0.000004179966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001567164,"about_ca_topic_score_gemma":0.008719074,"domain_scores_codex":[0.9994461,0.00008756996,0.00009787407,0.0001601435,0.00005900523,0.000149326],"domain_scores_gemma":[0.9992693,0.0002175104,0.0001155708,0.0003455721,0.000007418803,0.00004460286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002211947,0.00003872785,0.9757752,0.00001376381,0.00008026818,0.000006093944,0.01590269,0.0004216755,0.0008938819,0.001215175,0.002795127,0.002835277],"study_design_scores_gemma":[0.0003425456,0.000106163,0.9917843,0.000004195797,0.00002950578,0.000003309357,0.003427389,0.001030385,0.00005248005,0.002929551,0.0001860921,0.0001041068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703512,0.00001495456,0.002463574,0.02673867,0.0001128342,0.0001915115,0.0000471641,0.000009643999,0.0000704647],"genre_scores_gemma":[0.9906998,0.00001814268,0.002506536,0.006661681,0.00001130587,0.00001563692,0.00002124023,0.000004836301,0.00006083285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0203486,"threshold_uncertainty_score":0.4924204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05377062890756562,"score_gpt":0.3302279744515207,"score_spread":0.2764573455439551,"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."}}