{"id":"W6912658760","doi":"10.5281/zenodo.4284853","title":"VerdICT – environmental impact of ICT - World, Canada, Quebec","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Green IT and Sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Information and Communications Technology; Environmental impact assessment; Resource (disambiguation); Information technology; Verdict; Licensee","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006533773,0.0007626001,0.0003471512,0.003821752,0.00247973,0.003316159,0.001192102,0.0004721057,0.01959633],"category_scores_gemma":[0.001434796,0.0002164594,0.0006957037,0.006714127,0.0006441422,0.0007915896,0.001053662,0.0009082585,0.001251283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06266675,"about_ca_system_score_gemma":0.05413612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9975252,"about_ca_topic_score_gemma":0.9982719,"domain_scores_codex":[0.998722,0.00005830406,0.00003070118,0.00009490047,0.0008301846,0.0002638496],"domain_scores_gemma":[0.9973079,0.00008854788,0.00009885637,0.00006385118,0.002195526,0.0002453146],"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":[0.0005295934,0.0002212595,0.1314123,0.00109369,0.0005146337,0.0009547019,0.0008891234,0.01735923,0.001968412,0.02811733,0.671883,0.1450566],"study_design_scores_gemma":[0.00005256441,0.00007042339,0.4412465,0.0004231237,0.0001187514,0.0002421048,0.002544913,0.005789883,0.002053319,0.001401388,0.5459011,0.0001558135],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.132316,0.01078285,0.003226023,0.006967571,0.0005901822,0.0003923146,0.5223181,0.0008635718,0.3225435],"genre_scores_gemma":[0.6082073,0.007587596,0.005040073,0.001245401,0.00008477469,0.0002331743,0.1614309,0.0004915058,0.2156793],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06266675,"threshold_uncertainty_score":0.4546812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272613745299085,"score_gpt":0.1926937862360388,"score_spread":0.179967648783048,"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."}}