{"id":"W2766278473","doi":"10.1111/2041-210x.12931","title":"Growing the biphasic framework: Techniques and recommendations for fitting emerging growth models","year":2017,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Killam Trusts","keywords":"Markov chain Monte Carlo; Computer science; Trait; Inference; Bayesian probability; Econometrics; Bayesian inference; Machine learning; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00193184,0.00007135788,0.0001084012,0.00003746474,0.001643778,0.00002023563,0.0001247467,0.000100967,0.00002263664],"category_scores_gemma":[0.0005966718,0.00005867751,0.00001638033,0.00003991758,0.0003300152,0.0004170386,0.0003547262,0.000140419,9.959628e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005253033,"about_ca_system_score_gemma":0.000002081227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008134216,"about_ca_topic_score_gemma":0.0005781237,"domain_scores_codex":[0.9992794,0.0001748168,0.0001303427,0.0002033679,0.0000232,0.0001889112],"domain_scores_gemma":[0.9991795,0.0005771796,0.00009496809,0.0001291889,0.000005324511,0.0000138549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000185986,0.00002252825,0.858403,0.00001987183,0.00002515699,0.000001071659,0.0007907501,0.00007357588,0.00008798608,0.06519651,0.001999782,0.07336116],"study_design_scores_gemma":[0.0001366665,0.00003763439,0.5648213,0.00001205788,0.00002058467,0.000002937095,0.0003930626,0.007839367,0.00007916567,0.4252037,0.001380937,0.00007266422],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2589555,0.0001640483,0.6850103,0.04551055,0.0005412235,0.0007637579,0.000002570336,0.00006503159,0.008987024],"genre_scores_gemma":[0.6132523,0.0003337282,0.3856134,0.0004607956,0.00002947024,0.0001878879,8.11726e-7,0.000004515286,0.0001170498],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3600071,"threshold_uncertainty_score":0.999656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04247905937719307,"score_gpt":0.3746407290925988,"score_spread":0.3321616697154057,"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."}}