{"id":"W2754010351","doi":"10.1002/ecs2.1953","title":"Utilizing gradient simulations for quantifying community‐level resistance and resilience","year":2017,"lang":"en","type":"article","venue":"Ecosphere","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Ordination; Resistance (ecology); Disturbance (geology); Resilience (materials science); Ecosystem; Ecology; Gradient analysis; Environmental science; Environmental resource management; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003758612,0.0001155767,0.0001330753,0.000007544902,0.002864303,0.0001723468,0.0005195275,0.00005246219,0.0001540372],"category_scores_gemma":[0.0002297149,0.0001071264,0.00003777533,0.00004364982,0.0002369318,0.000316894,0.0003020452,0.0001394854,0.00004904408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006920636,"about_ca_system_score_gemma":0.000008148286,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006473645,"about_ca_topic_score_gemma":0.04528236,"domain_scores_codex":[0.9991174,0.00004586599,0.0001852857,0.0002528343,0.0001340507,0.0002645331],"domain_scores_gemma":[0.9988465,0.0001840253,0.0001669477,0.0006992445,0.00001291996,0.00009033955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002087271,0.0005773975,0.8193455,0.0008692506,0.00007798598,0.00002948788,0.007377355,0.03767641,0.02426929,0.04332474,0.02588981,0.04035405],"study_design_scores_gemma":[0.0006650668,0.00007131004,0.8469671,0.0002191644,0.00001814704,0.000005829155,0.001172483,0.1292286,0.00044043,0.006393428,0.01438501,0.000433438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525759,0.000118621,0.002487762,0.000263264,0.0001458935,0.0003058599,0.00004957999,0.00002421057,0.04402893],"genre_scores_gemma":[0.9902098,0.00002823884,0.006504184,0.00003816963,0.00001604035,0.00001564573,0.000003504508,0.0000130027,0.003171392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09155218,"threshold_uncertainty_score":0.9984338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0682682890152857,"score_gpt":0.3078058746001364,"score_spread":0.2395375855848507,"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."}}