{"id":"W4416537311","doi":"10.5593/sgem2025/4.1/s17.24","title":"EDUCATING FOR A CIRCULAR FUTURE: DIGITAL INNOVATIONS, INTERDISCIPLINARY LEARNING, AND GLOBAL POLICY INSIGHTS IN INDUSTRIAL SYMBIOSIS","year":2025,"lang":"","type":"article","venue":"International Multidisciplinary Scientific GeoConference SGEM ...","topic":"Sustainable Industrial Ecology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Digital transformation; Sustainability; Legislature; Incentive; Workflow; Resource (disambiguation); Digital Revolution; Industrial symbiosis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001057491,0.0008007291,0.0007968125,0.002836471,0.001162295,0.002217932,0.001266134,0.0009325447,0.0001608769],"category_scores_gemma":[0.002493378,0.0009357571,0.0002490477,0.004083711,0.0008112989,0.001670281,0.002629924,0.001322648,0.00004493977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003101217,"about_ca_system_score_gemma":0.003894931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001481998,"about_ca_topic_score_gemma":0.000201853,"domain_scores_codex":[0.9944292,0.0001728548,0.001801214,0.001773934,0.0005967509,0.001226011],"domain_scores_gemma":[0.9968752,0.0006659033,0.0004553956,0.0005630216,0.001174851,0.0002655914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001110675,0.002098246,0.4750945,0.0008935377,0.001297071,0.0001214506,0.009803015,0.01765102,0.0006070172,0.1323348,0.004674257,0.3543145],"study_design_scores_gemma":[0.01891898,0.0007205812,0.2055849,0.003655484,0.0002847121,0.0001047851,0.1008479,0.3855067,0.0002830923,0.1710591,0.1092934,0.003740365],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423642,0.0008382805,0.002264144,0.0172841,0.02428376,0.002953123,0.000565109,0.0002035009,0.009243844],"genre_scores_gemma":[0.992105,0.00003688748,0.000383437,0.00002938327,0.001791459,0.0004749669,0.0007215952,0.00004892297,0.004408318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3678556,"threshold_uncertainty_score":0.9993093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801508479204307,"score_gpt":0.3131511249047309,"score_spread":0.2951360401126878,"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."}}