{"id":"W1487085668","doi":"10.5751/es-02805-140136","title":"Linking Hunter Knowledge with Forest Change to Understand Changing Deer Harvest Opportunities in Intensively Logged Landscapes","year":2009,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Northwest Research Station; Alaska Department of Fish and Game; U.S. Department of Agriculture; U.S. Forest Service; Massachusetts Department of Fish and Game; National Science Foundation","keywords":"Geography; Ecology; Agroforestry; Environmental resource management; Biology; Environmental science","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":[0.003161412,0.0003228739,0.0005428877,0.003062341,0.0007075024,0.002063892,0.0009882122,0.001506665,0.004677583],"category_scores_gemma":[0.02153724,0.0004767194,0.0006229361,0.002054124,0.001158231,0.004430925,0.002027108,0.001467871,0.0002334675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009299553,"about_ca_system_score_gemma":0.000844968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01769109,"about_ca_topic_score_gemma":0.04026808,"domain_scores_codex":[0.9989905,0.0003668068,0.0000635644,0.0002592113,0.0001461662,0.0001737546],"domain_scores_gemma":[0.9808898,0.01289145,0.004021733,0.0007134409,0.0007322301,0.0007512903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000157592,0.0003838362,0.9735982,0.0001048559,0.0002718351,0.000103441,0.003512783,0.0008280513,0.0002047501,0.0002946956,0.0003839723,0.02015611],"study_design_scores_gemma":[0.00001328332,0.0001059747,0.989656,0.00008247729,0.0001412173,0.0001275365,0.004293077,0.002182879,0.0001311168,0.002684101,0.0005626727,0.00001980318],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964923,0.0005627864,0.0005285447,0.0005696407,0.000005490746,0.00001063436,0.0002595355,0.000004899211,0.001566087],"genre_scores_gemma":[0.9985777,0.0003181078,0.0006374884,0.00005860897,0.000006658697,0.00001431289,0.0002274989,0.000002258236,0.0001573652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01769109,"threshold_uncertainty_score":0.03517622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04094675741485031,"score_gpt":0.2370898341411406,"score_spread":0.1961430767262902,"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."}}