{"id":"W4393905742","doi":"10.1093/forestry/cpae017","title":"The impact of natural constraints in linear regression of log transformed response variables","year":2024,"lang":"en","type":"article","venue":"Forestry An International Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Northern Research Station; U.S. Forest Service","keywords":"Statistics; Estimator; Mathematics; Ordinary least squares; Context (archaeology); Variable (mathematics); Econometrics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0418994,0.0005922368,0.000611107,0.0006011829,0.000505268,0.00167943,0.001168169,0.0009451205,0.001179331],"category_scores_gemma":[0.1901435,0.0005213544,0.0006349528,0.00109847,0.00165088,0.00196803,0.001538195,0.001461804,0.0002436302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405141,"about_ca_system_score_gemma":0.001549063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004880329,"about_ca_topic_score_gemma":0.00485141,"domain_scores_codex":[0.9578917,0.03407576,0.001529319,0.002448474,0.003575803,0.0004789652],"domain_scores_gemma":[0.6374297,0.3383263,0.01200722,0.007940212,0.003865269,0.0004313052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0012979,0.0004696672,0.1710241,0.0007621754,0.0006789701,0.0009069911,0.001229325,0.5724317,0.01013628,0.05175325,0.001648928,0.1876607],"study_design_scores_gemma":[0.00006444084,0.0007288367,0.06649926,0.0002510308,0.000103315,0.0004138557,0.0002689166,0.8886361,0.009370022,0.03035268,0.003200127,0.0001115133],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3451509,0.001008032,0.6484101,0.0008171783,0.00009869417,0.0001843818,0.000272316,0.0004078182,0.003650496],"genre_scores_gemma":[0.9362082,0.0001466618,0.06240278,0.000217985,0.00002978466,0.0001763173,0.000172693,0.0001023958,0.0005430337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0418994,"threshold_uncertainty_score":0.2215878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721972158754328,"score_gpt":0.3919615090190173,"score_spread":0.3647417874314741,"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."}}