{"id":"W4394082777","doi":"10.6084/m9.figshare.21119371","title":"Behavioural adjustments of predators and prey to wind speed in the boreal forest data and code","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Predation; Taiga; Boreal; Geography; Code (set theory); Ecology; Environmental science; Biology; Forestry; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001718543,0.001470792,0.001286302,0.002294159,0.0007597564,0.001583593,0.002294167,0.001385,0.08396184],"category_scores_gemma":[0.00782295,0.0008806825,0.001425675,0.004109762,0.0003999505,0.001081504,0.001492528,0.001496531,0.03726545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102913,"about_ca_system_score_gemma":0.001750689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04631852,"about_ca_topic_score_gemma":0.0742277,"domain_scores_codex":[0.9988307,0.0002078179,0.0001654056,0.0003549077,0.0002656589,0.000175487],"domain_scores_gemma":[0.9961621,0.001187497,0.0006157551,0.0009003509,0.0008142522,0.0003200893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001002156,0.00002755097,0.006247354,0.000765816,0.0001215191,0.00002450744,0.00005663727,0.0008456302,0.0001897277,0.000803009,0.9878471,0.002970932],"study_design_scores_gemma":[0.0006739956,0.00005233346,0.106157,0.0007057583,0.0002148319,0.0001430107,0.0001529611,0.001485208,0.0005392178,0.004151264,0.8855617,0.0001627503],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002843977,0.00002634681,0.00009286778,0.00003944374,0.00001949559,0.00001095714,0.9989415,0.0001514885,0.0004334829],"genre_scores_gemma":[0.001788995,0.00004338492,0.0008846149,0.00007146617,0.00001285596,0.0002262482,0.9958851,0.0002007519,0.0008866868],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08396184,"threshold_uncertainty_score":0.2808803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06208970902235362,"score_gpt":0.29349205887528,"score_spread":0.2314023498529263,"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."}}