{"id":"W1974608358","doi":"10.14214/sf.456","title":"Improvement of low level bark beetle damage estimates with adaptive cluster sampling","year":2010,"lang":"en","type":"article","venue":"Silva Fennica","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pinus contorta; Infestation; Mountain pine beetle; Hectare; Dendroctonus; Forestry; Sampling (signal processing); Bark beetle; Biology; Agroforestry; Ecology; Bark (sound); Geography; Agronomy; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002099284,0.0003772062,0.0003273199,0.001097171,0.000281027,0.0005911962,0.0007862992,0.0002987663,0.001292604],"category_scores_gemma":[0.00765634,0.0001724702,0.0002691931,0.000866513,0.000255822,0.0004893232,0.0005272718,0.0003107474,0.0005008371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003449647,"about_ca_system_score_gemma":0.0004281438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01381411,"about_ca_topic_score_gemma":0.03466788,"domain_scores_codex":[0.9988679,0.0005046415,0.00006426029,0.000233727,0.0002754466,0.00005407379],"domain_scores_gemma":[0.9964799,0.001618208,0.0003653289,0.0004383096,0.001042289,0.00005602115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004784534,0.0002594765,0.1677211,0.0002119519,0.000287912,0.0001719417,0.00063591,0.1310887,0.05191721,0.001514361,0.00252628,0.6431867],"study_design_scores_gemma":[0.00002458398,0.0001668562,0.1073793,0.00002267999,0.00004738659,0.0001993451,0.0002471985,0.8749893,0.01429953,0.0007714506,0.001800789,0.00005157746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3076125,0.00009111452,0.6884445,0.00007878238,0.00002179557,0.0001927508,0.0005085524,0.001132084,0.001917914],"genre_scores_gemma":[0.5736336,0.00003596601,0.4245476,0.00003340933,0.00001026809,0.0001115833,0.0006230228,0.00008854783,0.0009162096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01381411,"threshold_uncertainty_score":0.02746743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584528156156294,"score_gpt":0.2292620024364845,"score_spread":0.2134167208749215,"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."}}