{"id":"W186226966","doi":"","title":"HOW CAN TEMPORAL CONSIDERATIONS OPEN NEW OPPORTUNITIES FOR LCA INDUSTRY APPLICATIONS?","year":2013,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Carbon footprint; Computer science; Footprint; Identification (biology); Ecological footprint; Resource (disambiguation); System dynamics; Natural resource; Risk analysis (engineering); Sustainability; Greenhouse gas; Artificial intelligence; Business","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006431708,0.0006492981,0.0006323406,0.0001540838,0.000600071,0.001674367,0.001318699,0.001222869,0.001093911],"category_scores_gemma":[0.0002201813,0.0006734894,0.0002533433,0.000167141,0.000464047,0.0007929556,0.003480031,0.001322907,0.00001950104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002329348,"about_ca_system_score_gemma":0.000748768,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08210581,"about_ca_topic_score_gemma":0.01389769,"domain_scores_codex":[0.9969518,0.0001681984,0.0006038086,0.0009711452,0.0004200758,0.0008849616],"domain_scores_gemma":[0.9966953,0.00013894,0.0004965139,0.001726601,0.00004178948,0.0009008052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001378508,0.001548227,0.3241584,0.0004181299,0.0003198523,0.00007871262,0.001715152,0.01321195,0.002620572,0.04126622,0.5294643,0.08506062],"study_design_scores_gemma":[0.002370914,0.0005484667,0.1495988,0.0001828791,0.0003830343,0.00017398,0.006832113,0.02427763,0.00822743,0.5011033,0.3020326,0.004268779],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2774746,0.0008345127,0.4799703,0.1967919,0.0003715081,0.03207746,0.002053144,0.00121557,0.009210953],"genre_scores_gemma":[0.8013575,0.0001175166,0.1355863,0.00571051,0.000254779,0.01370053,0.0005887969,0.0001388459,0.0425452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5238829,"threshold_uncertainty_score":0.9998192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843271376147181,"score_gpt":0.2734973825111754,"score_spread":0.2250646687497036,"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."}}