{"id":"W2783877980","doi":"10.1002/wsb.847","title":"Wildlife biology, big data, and reproducible research","year":2018,"lang":"en","type":"article","venue":"Wildlife Society Bulletin","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Memorial University of Newfoundland","funders":"","keywords":"Wildlife; Scripting language; Data science; Best practice; Field (mathematics); Process (computing); Quality (philosophy); Computer science; Inefficiency; Data quality; Ecology; Biology; Political science; Business; Marketing","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.312825,0.000666312,0.0009897377,0.006753909,0.008078317,0.02743821,0.004472921,0.004275905,0.006468418],"category_scores_gemma":[0.4003417,0.001070263,0.001035185,0.009386665,0.06097822,0.02540722,0.0202378,0.008181388,0.001075941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01062759,"about_ca_system_score_gemma":0.04316463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006045387,"about_ca_topic_score_gemma":0.007127287,"domain_scores_codex":[0.667734,0.2536512,0.01727599,0.01412206,0.04350096,0.003715748],"domain_scores_gemma":[0.2506304,0.5440353,0.04734068,0.1008027,0.04464108,0.0125498],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000804328,0.0001309264,0.01975834,0.002404558,0.0001515975,0.0004993503,0.02961574,0.001785772,0.0006501232,0.7641678,0.03109019,0.1496651],"study_design_scores_gemma":[0.00005515129,0.0001207108,0.008272252,0.004211249,0.00005127641,0.0004546703,0.01605394,0.001485544,0.001051672,0.7323857,0.2357517,0.0001060552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0349345,0.02271826,0.2544592,0.5533781,0.004587455,0.001129543,0.0005673977,0.001053508,0.1271721],"genre_scores_gemma":[0.6758151,0.01700745,0.2539465,0.03731308,0.003642693,0.002605839,0.0005395772,0.0009050069,0.008224739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.687175,"threshold_uncertainty_score":0.8474091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1432863690438766,"score_gpt":0.3486182328963437,"score_spread":0.2053318638524672,"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."}}