{"id":"W2053325388","doi":"10.1111/j.1365-2745.2007.01215.x","title":"Measuring the components of competition along productivity gradients","year":2007,"lang":"en","type":"article","venue":"Journal of Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Crowding; Competition (biology); Productivity; Ecology; Range (aeronautics); Biomass (ecology); Relative species abundance; Econometrics; Abundance (ecology); Economics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001840083,0.0002208418,0.000438319,0.002539147,0.0003600307,0.0009827329,0.0004905167,0.0002116015,0.001041588],"category_scores_gemma":[0.005310726,0.0001683382,0.0002469702,0.001895002,0.0006698323,0.0007567278,0.0007140256,0.0004439293,0.0001942561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005180094,"about_ca_system_score_gemma":0.0002711955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002048154,"about_ca_topic_score_gemma":0.003632409,"domain_scores_codex":[0.9987441,0.0003325063,0.0001041694,0.000188128,0.0005652347,0.000065906],"domain_scores_gemma":[0.9947966,0.002833525,0.001013848,0.0003429685,0.000713431,0.0002997157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002915476,0.00008060224,0.8552942,0.0004427101,0.0004836061,0.0001533735,0.0006072771,0.005225881,0.06924097,0.002860822,0.00064233,0.0646767],"study_design_scores_gemma":[0.000005167085,0.0001162961,0.9841218,0.00002831971,0.00006801252,0.0001813314,0.0002656363,0.004974147,0.006109994,0.002537662,0.001550685,0.0000409298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866086,0.001251873,0.008101194,0.00007474441,0.00001747863,0.00001618502,0.0006106221,0.00003740199,0.003281891],"genre_scores_gemma":[0.9963541,0.0001781061,0.002784043,0.0000202106,0.0000139227,0.00001493007,0.0003549407,0.000009847718,0.000269968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002539147,"threshold_uncertainty_score":0.009731412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224596862120555,"score_gpt":0.2371035252485215,"score_spread":0.2146438390364659,"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."}}