{"id":"W4285987729","doi":"10.1098/rspb.2022.0938","title":"Data rescue: saving environmental data from extinction","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Research Data Management Practices","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; Carleton University; Université de Montréal; University of British Columbia; McGill University; University of Regina","funders":"Université de Montréal; Natural Sciences and Engineering Research Council of Canada; McGill University; H2020 Marie Skłodowska-Curie Actions; University of Regina","keywords":"Metadata; Data sharing; Computer science; Reuse; Best practice; Conflation; Data science; Environmental data; Prioritization; Key (lock); Risk analysis (engineering); World Wide Web; Business; Process management; Computer security; Engineering; Ecology; Political science","routes":{"ca_aff":true,"ca_fund":true,"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","open_science"],"consensus_categories":[],"category_scores_codex":[0.04222919,0.001389829,0.001358797,0.009085312,0.005360453,0.01335022,0.008418516,0.004905475,0.005370747],"category_scores_gemma":[0.1059684,0.001414146,0.001820789,0.007565306,0.009758311,0.03307516,0.02581815,0.006658844,0.006334325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002142828,"about_ca_system_score_gemma":0.01000801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006145849,"about_ca_topic_score_gemma":0.007357181,"domain_scores_codex":[0.9703496,0.01203507,0.004728318,0.003364595,0.008342986,0.001179428],"domain_scores_gemma":[0.9077556,0.02465701,0.007751327,0.04092417,0.01508995,0.003821879],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002277358,0.0001957875,0.01044509,0.00239585,0.0001718529,0.0004949233,0.01027076,0.008373396,0.006210909,0.2650587,0.1476024,0.5485525],"study_design_scores_gemma":[0.00007004161,0.000125118,0.003166594,0.002779602,0.00009690811,0.0009411862,0.006342275,0.01170307,0.01216509,0.3216825,0.6406898,0.0002377987],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009130931,0.002966959,0.9062251,0.04450179,0.0009384975,0.001033152,0.002540848,0.008987608,0.02367503],"genre_scores_gemma":[0.05160842,0.003165581,0.9266773,0.004204928,0.0003411466,0.0008349497,0.005283462,0.001604637,0.006279564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9915815,"threshold_uncertainty_score":0.223332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2062247505824426,"score_gpt":0.3414316595047892,"score_spread":0.1352069089223465,"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."}}