{"id":"W3154832708","doi":"10.1139/cjfr-2020-0490","title":"Efficient synthetic generation of ecological data with preset spatial association of individuals","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Food and Agriculture; National Authority for Scientific Research and Innovation; U.S. Department of Agriculture","keywords":"Index (typography); Statistics; Dominance (genetics); Factorial; Spatial analysis; Mathematics; Ecology; Forest ecology; Spatial distribution; Computer science; Forestry; Ecosystem; Geography; Biology","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":[0.002354868,0.0004236628,0.0005359782,0.0004608949,0.0003454763,0.0005653123,0.001064173,0.0005271927,0.001543458],"category_scores_gemma":[0.008566678,0.0002972556,0.0005786069,0.0005751711,0.0005875718,0.0006430969,0.0007251315,0.0006584611,0.0002493276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005399763,"about_ca_system_score_gemma":0.0006395132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001637962,"about_ca_topic_score_gemma":0.00196732,"domain_scores_codex":[0.9992129,0.0003868181,0.00005627935,0.0001544769,0.0001340498,0.00005559833],"domain_scores_gemma":[0.9905579,0.006337505,0.0005439069,0.001811617,0.0005793062,0.0001697204],"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":[0.0004271561,0.0003476336,0.01115088,0.0002499929,0.0001214155,0.0001573553,0.0002462665,0.9263361,0.01573139,0.01177482,0.001229129,0.03222781],"study_design_scores_gemma":[0.00004951672,0.0001150519,0.001393474,0.000009539687,0.0000170935,0.00004321372,0.00004472409,0.9855478,0.005687638,0.00602564,0.001049202,0.00001718823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4514881,0.0001062153,0.5432528,0.0001611798,0.00009348436,0.000429019,0.001409771,0.001116376,0.001942974],"genre_scores_gemma":[0.791993,0.00005345116,0.2048118,0.00004932597,0.00001447245,0.0008868799,0.001589469,0.00008417319,0.0005174972],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002354868,"threshold_uncertainty_score":0.01245391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05553240758028846,"score_gpt":0.29718229507495,"score_spread":0.2416498874946616,"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."}}