{"id":"W4412017458","doi":"10.1145/3715336.3735734","title":"\"Down to Earth\": Design Considerations for AI for Sustainability from the Environmental and Climate Movement","year":2025,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Washington; National Science Foundation","keywords":"Sustainability; Movement (music); Earth (classical element); Environmental movement; Computer science; Climate change; Environmental resource management; Environmental science; Political science; Geology; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02239223,0.0008154919,0.0004861625,0.001952048,0.0102953,0.01345952,0.002286737,0.006074265,0.004545775],"category_scores_gemma":[0.02724291,0.0006908011,0.0008578226,0.001058046,0.03504135,0.01163476,0.007995903,0.005831792,0.0007294641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008688659,"about_ca_system_score_gemma":0.01414333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01464087,"about_ca_topic_score_gemma":0.02457226,"domain_scores_codex":[0.9747511,0.02080167,0.0005634226,0.0007762351,0.0020447,0.001062936],"domain_scores_gemma":[0.9782975,0.01625559,0.0006428159,0.001161564,0.00236401,0.001278388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006764906,0.00005430847,0.002027307,0.0004206649,0.00002144934,0.0004564749,0.1030467,0.001945553,0.001588274,0.8544617,0.006644166,0.02926579],"study_design_scores_gemma":[0.0000439913,0.00009930936,0.001065287,0.0008893795,0.00006485535,0.0004798819,0.07406216,0.003583594,0.001818479,0.4913809,0.4264534,0.00005884273],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09015819,0.008080383,0.2449497,0.3444879,0.001627257,0.000406719,0.0000819756,0.0006435533,0.3095644],"genre_scores_gemma":[0.867443,0.003203919,0.09363466,0.01766736,0.0002341957,0.000706428,0.00006345845,0.0003143967,0.01673246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02239223,"threshold_uncertainty_score":0.1184229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02118853202417842,"score_gpt":0.2932951959736052,"score_spread":0.2721066639494268,"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."}}