{"id":"W1778150177","doi":"10.1139/x2012-090","title":"Evaluating marginal and conditional predictions of taper models in the absence of calibration data","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundación Biodiversidad","keywords":"Calibration; Data set; Mathematics; Statistics; Random effects model; Set (abstract data type); Marginal model; Econometrics; Computer science; Regression analysis; Meta-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":[0.03976556,0.001684934,0.00151544,0.001844835,0.0005983653,0.001792332,0.002417865,0.001473439,0.001008327],"category_scores_gemma":[0.08288549,0.0008511198,0.002818947,0.001256104,0.00102107,0.003111086,0.002258206,0.001728525,0.0003834497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527941,"about_ca_system_score_gemma":0.002517727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008265888,"about_ca_topic_score_gemma":0.009398665,"domain_scores_codex":[0.9888713,0.007520069,0.0006400797,0.001519383,0.001155539,0.000293725],"domain_scores_gemma":[0.898187,0.09056965,0.002614257,0.004224436,0.003970122,0.0004344949],"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":[0.002011353,0.0003100278,0.08443728,0.0008278189,0.001327796,0.0003736927,0.001092967,0.7777475,0.006569359,0.007386056,0.0007581735,0.1171581],"study_design_scores_gemma":[0.00005181154,0.0005873424,0.01553016,0.00005823228,0.0002690318,0.0001379311,0.0001702395,0.9752381,0.003537529,0.003908966,0.0004010539,0.0001096832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5576183,0.00070648,0.4386126,0.0001487578,0.00003009528,0.0002091081,0.0004470952,0.0009598621,0.001267699],"genre_scores_gemma":[0.8362408,0.000213978,0.1618872,0.00004621394,0.000009218262,0.0002260884,0.0006513261,0.0002652511,0.0004599379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03976556,"threshold_uncertainty_score":0.2103028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1597993066689035,"score_gpt":0.3783281888440311,"score_spread":0.2185288821751276,"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."}}