{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001379018,0.0000915565,0.0002169018,0.0001135301,0.00002019504,0.00002720419,0.0003906087,0.00002686174,0.0001005823],"category_scores_gemma":[0.0001844486,0.0000672554,0.0000575472,0.0001134617,0.0001008071,0.001138922,0.000162903,0.000160943,0.000002880073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006005223,"about_ca_system_score_gemma":0.0000433901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001721156,"about_ca_topic_score_gemma":0.0002014677,"domain_scores_codex":[0.9979394,0.000169287,0.0009253909,0.00007400174,0.0007747753,0.0001171482],"domain_scores_gemma":[0.9986761,0.0001964417,0.0008977369,0.0001468931,0.00003660575,0.00004618259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001124462,0.0002343086,0.9494213,0.00002764303,0.00002330949,0.000001726865,0.0003824455,0.04561808,0.001463965,0.000619408,0.00007861205,0.002016692],"study_design_scores_gemma":[0.0008298612,0.00009960777,0.9680179,0.0001145283,0.000007110687,0.00000669402,0.001786342,0.02783789,0.0003140852,0.0003740044,0.0005429962,0.00006896513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959521,0.00002756947,0.002728608,0.0003314761,0.0002354372,0.0001400467,0.00001370101,0.000001951553,0.0005691582],"genre_scores_gemma":[0.9989994,0.00003453845,0.0008358156,0.0000547647,0.00004087074,0.000003842611,0.00001810498,0.000004374605,0.000008360636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01859654,"threshold_uncertainty_score":0.2742596,"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."}}