{"id":"W2062283927","doi":"10.1016/j.egypro.2014.11.271","title":"CO2 Pipeline Infrastructure – Lessons Learnt","year":2014,"lang":"en","type":"article","venue":"Energy Procedia","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"SNC-Lavalin (Canada)","funders":"","keywords":"Pipeline (software); Pipeline transport; Variety (cybernetics); Identification (biology); Set (abstract data type); Computer science; Engineering; Database; Data science; Operating system; Environmental engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006968021,0.00008521888,0.00006864759,0.00001345475,0.000103537,0.00001989325,0.0001167622,0.00005949961,0.00759957],"category_scores_gemma":[0.00009871983,0.00007145128,0.00002897179,0.0001080803,0.00007250373,0.0001024885,0.00006520471,0.0001032982,0.0004750746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000392146,"about_ca_system_score_gemma":0.00001068852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001878785,"about_ca_topic_score_gemma":0.0006673154,"domain_scores_codex":[0.9994023,0.00002525227,0.0001081754,0.0001861431,0.0001264652,0.0001516213],"domain_scores_gemma":[0.9997134,0.000029161,0.00004330417,0.000133164,0.000009768347,0.00007124829],"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":[0.00004091073,0.0001690376,0.02253033,0.000008818577,0.00001517547,0.000008672495,0.0007548406,0.05159013,0.01737987,0.1991502,0.5077398,0.2006122],"study_design_scores_gemma":[0.0001187071,0.00003780094,0.01779971,0.000002609075,0.000004225123,0.00001821632,0.00003300443,0.007256159,0.002351493,0.006520698,0.9657392,0.0001181729],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1014547,0.00001842153,0.0363511,0.01023437,0.0007862257,0.0000843814,0.000004737626,0.0002732792,0.8507928],"genre_scores_gemma":[0.9760827,0.000009739172,0.0008454201,0.001482356,0.0001559729,0.00001113336,0.00001645362,0.000006064755,0.02139014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.874628,"threshold_uncertainty_score":0.9933076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00620630610717503,"score_gpt":0.2278386001046719,"score_spread":0.2216322939974968,"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."}}