{"id":"W2003760200","doi":"10.1139/x04-163","title":"Modeling response curves and testing treatment effects in repeated measures experiments: a multilevel nonlinear mixed-effects model approach","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Mixed model; Random effects model; Autoregressive model; Linear model; Multilevel model; Nonlinear system; Growth curve (statistics); Mathematics; Nonlinear regression; Flexibility (engineering); Statistics; Econometrics; Growth model; Regression analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01908085,0.001602518,0.00239902,0.001539114,0.0007797349,0.001521483,0.004128287,0.001687457,0.004329861],"category_scores_gemma":[0.04742473,0.0009253134,0.004181186,0.001706504,0.0009088776,0.001593239,0.001725753,0.003015972,0.0008125904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552152,"about_ca_system_score_gemma":0.001864892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005452979,"about_ca_topic_score_gemma":0.006145977,"domain_scores_codex":[0.9861063,0.008720442,0.000574994,0.002588829,0.001599371,0.0004100934],"domain_scores_gemma":[0.9718497,0.02142298,0.001714976,0.00320949,0.001542517,0.0002604075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003113057,0.002213318,0.03702553,0.002508065,0.005014963,0.0004837576,0.002736847,0.4244641,0.08380879,0.08682539,0.0049818,0.3468245],"study_design_scores_gemma":[0.0002281047,0.001928846,0.0115817,0.00007869771,0.000518939,0.00007858986,0.0001561441,0.9276662,0.01346659,0.03863974,0.005501914,0.0001545159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02158123,0.00006016821,0.9760709,0.00008057411,0.00004015038,0.0005680562,0.0004984866,0.0005770816,0.0005233533],"genre_scores_gemma":[0.134134,0.0001251791,0.8561321,0.0001019443,0.000031944,0.007123912,0.0008339736,0.0002300433,0.001286958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01908085,"threshold_uncertainty_score":0.1009104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08067126266880707,"score_gpt":0.3199911046582922,"score_spread":0.2393198419894851,"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."}}