{"id":"W4404195587","doi":"10.1016/j.dendro.2024.126274","title":"CTRing: An R package to extract wood density profiles from computed tomography images of discs and logs","year":2024,"lang":"en","type":"article","venue":"Dendrochronologia","topic":"Forest ecology and management","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Dynamics (Canada); Ministry of Natural Resources and Wildlife; Institut National de la Recherche Scientifique; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Institut national de la recherche scientifique","keywords":"Computed tomography; Tomography; Geology; Medicine; Radiology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001283757,0.0001360624,0.0001634658,0.00008268271,0.00007262373,0.00004001561,0.0002173438,0.00008940993,0.000631511],"category_scores_gemma":[0.00001620547,0.0001165559,0.0000401087,0.0002222557,0.0002772543,0.0002070341,0.0003850723,0.0001233194,0.0002285904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003916529,"about_ca_system_score_gemma":0.000005964599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002203637,"about_ca_topic_score_gemma":0.0005302517,"domain_scores_codex":[0.9990345,0.0000435907,0.0001570033,0.0004310023,0.0001077913,0.0002261373],"domain_scores_gemma":[0.9995625,0.00005866254,0.0000352438,0.0002537963,0.000004172648,0.00008560313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001680473,0.0008568667,0.7244214,0.0002086776,0.0004414503,0.0008209912,0.002511184,0.0006914202,0.1369567,0.01414707,0.02906525,0.08971091],"study_design_scores_gemma":[0.0001309268,0.0002578539,0.9725296,0.00002325103,0.0000357834,0.000007641992,0.0001048211,0.0004812557,0.02259821,0.002734579,0.000931595,0.0001644508],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902212,0.0002671559,0.007623653,0.0001763019,0.0001676974,0.0002493302,0.00002887228,0.0001312836,0.001134483],"genre_scores_gemma":[0.9944891,0.00001797061,0.005207736,0.00009457843,0.00002311826,0.00001586715,0.00002559476,0.000009431234,0.000116634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2481082,"threshold_uncertainty_score":0.6914603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008025315812419418,"score_gpt":0.2273163393789245,"score_spread":0.2192910235665051,"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."}}