{"id":"W2751841878","doi":"10.5849/forsci.14-098","title":"A Panel Data Analysis of Coastal Log Supply in British Columbia","year":2015,"lang":"en","type":"article","venue":"Forest Science","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Stumpage; Panel data; Environmental science; Agricultural economics; Geography; Economics; Econometrics","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00105364,0.00005657933,0.0001589373,0.0001345513,0.00007245429,0.0001809765,0.001498963,0.00002370106,0.001259578],"category_scores_gemma":[0.0002033123,0.00008028519,0.00002843787,0.003691554,0.001179274,0.0007900753,0.001424395,0.00005592288,0.0002159471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006645772,"about_ca_system_score_gemma":0.0000551669,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3035631,"about_ca_topic_score_gemma":0.8501019,"domain_scores_codex":[0.9983799,0.00002077236,0.0002063243,0.0004317714,0.0006201421,0.0003410882],"domain_scores_gemma":[0.9990587,0.00002081547,0.00007566609,0.0006777085,0.00001230126,0.0001548463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002086246,0.0000500455,0.9656628,0.000001329894,0.000006962941,0.00001181486,0.0001531532,0.01744345,0.00003265077,0.0001153751,0.01538312,0.001137174],"study_design_scores_gemma":[0.0001746324,0.00003589408,0.8792309,0.000006291321,0.0000356656,0.000001952715,0.00005634006,0.1168508,0.000001445615,0.0005506558,0.002960408,0.00009502456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912966,0.000008342537,0.00008063461,0.00005542341,0.00006537004,0.0001156801,0.0001037794,0.0000135627,0.008260553],"genre_scores_gemma":[0.9967977,0.000005757137,0.0005973525,0.00007448684,0.00001084095,0.000003904191,0.00006302373,0.000003975186,0.00244295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5465388,"threshold_uncertainty_score":0.9996534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0410791793222248,"score_gpt":0.2656008967517756,"score_spread":0.2245217174295508,"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."}}