{"id":"W2905755634","doi":"10.1002/ecm.1350","title":"Trajectory analysis in community ecology","year":2018,"lang":"en","type":"article","venue":"Ecological Monographs","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université de Montréal","funders":"Smithsonian Institution","keywords":"Trajectory; Perspective (graphical); Computer science; Variety (cybernetics); Variation (astronomy); Space (punctuation); Data science; Ecology; Domain (mathematical analysis); Community; Community structure; Data mining; Artificial intelligence; Mathematics; Habitat","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005871225,0.0001027694,0.0002320522,0.0001586892,0.0003832141,0.000006652015,0.0002418436,0.0001494336,0.005094963],"category_scores_gemma":[0.0001148112,0.00008666276,0.0001116422,0.001225942,0.0009834196,0.00006600231,0.0002077888,0.0003065839,0.0004750405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007707528,"about_ca_system_score_gemma":0.000003336954,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001470479,"about_ca_topic_score_gemma":0.08181611,"domain_scores_codex":[0.9987894,0.0004379039,0.0002140464,0.0002033482,0.00006423806,0.0002910275],"domain_scores_gemma":[0.999327,0.0003637217,0.0000589789,0.0001872797,0.000008638217,0.00005440538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001205033,0.0003254453,0.9977368,7.017838e-7,0.00006768738,0.000004462794,0.0004657591,0.0005286579,0.0000300007,0.000227445,0.0003589154,0.0002420877],"study_design_scores_gemma":[0.0001843434,0.000355304,0.9917392,4.013946e-7,0.00004585773,7.196614e-7,0.0001992036,0.001754953,0.000009375704,0.005313916,0.00029574,0.0001010544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683053,0.000008822149,0.00009818223,0.0001872489,0.00009825118,0.0001079534,0.000002569496,0.00004382044,0.03114782],"genre_scores_gemma":[0.9982383,0.00001755431,0.0007793029,0.0007623655,0.00001127242,0.00004255449,0.000006992198,0.000002969568,0.0001386992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08166906,"threshold_uncertainty_score":0.9958145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01526486304283983,"score_gpt":0.255590956932276,"score_spread":0.2403260938894361,"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."}}