{"id":"W2971770083","doi":"10.1088/1755-1315/323/1/012127","title":"Reducing water footprint of building sector: concrete with seawater and marine aggregates","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Recycled Aggregate Concrete Performance","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Seawater; Environmental science; Water use; Context (archaeology); Life-cycle assessment; Water scarcity; Urbanization; Freshwater ecosystem; Environmental engineering; Water resources; Natural resource economics; Environmental protection; Ecosystem; Oceanography; Ecology; Geography; Production (economics); Geology","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":[0.0001208879,0.0001772076,0.0001925943,0.00006349572,0.00009455001,0.00008380668,0.000145973,0.00003682073,0.0002232966],"category_scores_gemma":[0.000002362067,0.0001229254,0.00001312617,0.00007129103,0.0008265148,0.0005358001,0.0002075183,0.0001114345,0.000008445549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002112054,"about_ca_system_score_gemma":0.00001224772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007219869,"about_ca_topic_score_gemma":0.000009663921,"domain_scores_codex":[0.9989477,0.000008217323,0.0001575134,0.0003016041,0.0002126923,0.0003722821],"domain_scores_gemma":[0.9996191,0.00001101614,0.00003213169,0.0001958328,0.000008964933,0.0001330076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002573937,7.94845e-7,0.0137718,0.0000617813,0.000008073994,0.000003426293,0.0005704542,0.0005557106,0.9722386,0.00006069761,1.732484e-7,0.01270276],"study_design_scores_gemma":[0.0002597207,0.0001791206,0.02815548,0.00008192754,0.00000574519,0.0000935374,0.00027834,0.008463737,0.9618619,0.0000212769,0.0003793742,0.0002198388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980872,0.0001300785,0.00001815751,0.00003060287,0.00007772709,0.0001632937,0.00000537593,0.00004050164,0.001447062],"genre_scores_gemma":[0.9979444,0.0006055866,0.001248747,0.000007377588,0.00001221342,0.00000504608,0.000004464045,0.00001339556,0.000158776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01438369,"threshold_uncertainty_score":0.5012752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005256953913733041,"score_gpt":0.1638446449567112,"score_spread":0.1585876910429782,"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."}}