{"id":"W1990880182","doi":"10.1007/s11367-014-0783-5","title":"Temporal differentiation of background systems in LCA: relevance of adding temporal information in LCI databases","year":2014,"lang":"en","type":"article","venue":"The International Journal of Life Cycle Assessment","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Relevance (law); Computer science; Database; Temporal database; Product (mathematics); Life-cycle assessment; Data mining; Production (economics); Mathematics","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":[0.01531498,0.0007209291,0.001837576,0.006486726,0.001010682,0.007756193,0.002550427,0.001193185,0.002871414],"category_scores_gemma":[0.06621069,0.0008369281,0.001390327,0.01258975,0.0007461715,0.0128055,0.002526464,0.002149692,0.0008633458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146101,"about_ca_system_score_gemma":0.003132963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01793372,"about_ca_topic_score_gemma":0.01707978,"domain_scores_codex":[0.9920736,0.002406506,0.00170247,0.001573045,0.001897347,0.0003471668],"domain_scores_gemma":[0.9496932,0.02737508,0.004523188,0.01155027,0.005900142,0.0009581448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002434344,0.000798264,0.1909805,0.001708658,0.0008327245,0.0005527267,0.001417649,0.1507652,0.005808569,0.08067611,0.008772756,0.5552525],"study_design_scores_gemma":[0.0002474019,0.0004941347,0.06552083,0.001202261,0.001071112,0.0008519359,0.001658041,0.6773661,0.01687825,0.1767581,0.05768711,0.0002647227],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2267945,0.006392216,0.7180872,0.002923766,0.0004051562,0.000530682,0.02463646,0.002406406,0.01782354],"genre_scores_gemma":[0.6915417,0.001712717,0.2894087,0.0003442726,0.0002015415,0.0001983098,0.01504184,0.0003027676,0.001248179],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01793372,"threshold_uncertainty_score":0.08099431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01900453024643421,"score_gpt":0.30073433788926,"score_spread":0.2817298076428258,"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."}}