{"id":"W7076655975","doi":"10.5281/zenodo.16899346","title":"Productive yet wild: Reconciling timber harvesting and small mammal conservation via understory protection harvesting in managed boreal landscapes of Alberta, Canada","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Understory; Boreal; Taiga; Logging; Vegetation (pathology); Wildlife conservation; Range (aeronautics)","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":[0.0005747695,0.000779401,0.0004632257,0.00252725,0.001122937,0.001735458,0.001905766,0.0007299202,0.007402481],"category_scores_gemma":[0.00189843,0.0003592751,0.0005457826,0.005297569,0.0005840498,0.0005161281,0.0008947295,0.0007957758,0.002828613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01265544,"about_ca_system_score_gemma":0.0132577,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9880552,"about_ca_topic_score_gemma":0.9954528,"domain_scores_codex":[0.9996296,0.00003044618,0.00001803976,0.00007894224,0.000121607,0.0001214135],"domain_scores_gemma":[0.998941,0.000107708,0.00009670835,0.0001156654,0.0005417384,0.0001971882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000263978,0.00009688675,0.1114311,0.0005780103,0.0002234638,0.000202409,0.000543984,0.004582679,0.0004203386,0.003018615,0.8609927,0.01764586],"study_design_scores_gemma":[0.0001989364,0.00002473523,0.5254844,0.0005790885,0.0001740257,0.0001583449,0.00278647,0.006390684,0.0008920611,0.001735242,0.4614497,0.0001264159],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02934478,0.0007587228,0.0003256786,0.000464263,0.00004215054,0.00002926071,0.9646811,0.0002632116,0.004090821],"genre_scores_gemma":[0.04696051,0.0004035855,0.0009974797,0.0001233244,0.00001485856,0.00003826747,0.9452793,0.00008484311,0.00609782],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01265544,"threshold_uncertainty_score":0.09182203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04604817597139685,"score_gpt":0.1944467644423569,"score_spread":0.14839858847096,"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."}}