{"id":"W7033334435","doi":"","title":"Predicting Carbon Accumulation in Temperate Forests of Ontario Using a LiDAR-Initialized Growth-and-Yield Model","year":2019,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Diverse Perspectives in Modern Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Ironwood Pharmaceuticals, Incorporated","keywords":"Exclosure; Empirical probability; Tree (set theory); Precipitation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007829763,0.000324573,0.0007918242,0.0009184021,0.0001092873,0.00006213088,0.00026337,0.0003701063,0.00006002215],"category_scores_gemma":[0.00007254243,0.0004617853,0.0001502665,0.000318626,0.00006135833,0.001142997,0.0002065834,0.0004187856,0.000004694275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005092437,"about_ca_system_score_gemma":0.0003389587,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2596579,"about_ca_topic_score_gemma":0.01997435,"domain_scores_codex":[0.9985307,0.00002848382,0.0004085324,0.0006543898,0.00008774111,0.0002901566],"domain_scores_gemma":[0.998852,0.0000787395,0.0006849998,0.0002719337,0.00005026462,0.00006203517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004222039,0.00007061635,0.9571735,0.0002758652,0.0002015401,0.00001818321,0.01834702,0.004208411,0.00001032879,0.01918539,0.00007705023,0.000009900284],"study_design_scores_gemma":[0.0073584,0.0004309881,0.8405722,0.002439517,0.0004827769,0.000001006619,0.02795737,0.08300778,0.001850011,0.031588,0.0007119765,0.003599965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9420801,0.00009849758,0.0001616985,0.0003259714,0.0002940828,0.0004359623,0.0001264663,0.00005332468,0.05642393],"genre_scores_gemma":[0.9659038,0.000272014,0.00142719,0.00001401658,0.00002630072,0.000001354539,0.0001140354,0.00005160345,0.0321897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2396836,"threshold_uncertainty_score":0.9997834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03560945394723478,"score_gpt":0.2263369427766563,"score_spread":0.1907274888294216,"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."}}