{"id":"W6929069199","doi":"10.4121/14572929.v2","title":"Carbon stock map and uncertainty in plants of forested areas of Canada, 250m spatial resolution","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Terrain; Carbon stock; Canopy; Forest inventory; Spatial distribution; Quantile; Foothills; Tree canopy; Carbon sink; Stock (firearms)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001692374,0.0004457201,0.0002614876,0.002051321,0.000465926,0.000926685,0.0005127455,0.0002672916,0.001750049],"category_scores_gemma":[0.0007359613,0.0001681721,0.0004072698,0.002976324,0.0002341186,0.0002965819,0.0004272067,0.0002503132,0.0004468743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006638364,"about_ca_system_score_gemma":0.005781586,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9606151,"about_ca_topic_score_gemma":0.9617276,"domain_scores_codex":[0.9998314,0.000004366662,0.000005679061,0.00004172394,0.00008073563,0.00003607509],"domain_scores_gemma":[0.9994615,0.00003883881,0.0000338441,0.00002885726,0.0003946589,0.00004244864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004831102,0.0001151763,0.6331639,0.0005117656,0.0004471483,0.0004879051,0.0007540135,0.1524492,0.01410833,0.003615388,0.02807104,0.165793],"study_design_scores_gemma":[0.00002526794,0.00003076371,0.8175369,0.0001117748,0.00008043463,0.0001849152,0.0009317501,0.1410066,0.007230682,0.0008875798,0.03187581,0.00009757535],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8385698,0.0009309038,0.007962481,0.0002108178,0.00001412125,0.00004997343,0.1446742,0.001043336,0.006544395],"genre_scores_gemma":[0.9134589,0.000454592,0.01049761,0.00003722826,0.000005208264,0.00003132071,0.07287435,0.00005785546,0.002583041],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.0393849,"threshold_uncertainty_score":0.07923365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03270452710148333,"score_gpt":0.2523926452512895,"score_spread":0.2196881181498061,"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."}}