{"id":"W4403247726","doi":"10.1016/j.eiar.2024.107686","title":"Practices, events, and effects: Improving causal analysis with the geographic information from cultural mapping in Canada","year":2024,"lang":"en","type":"article","venue":"Environmental Impact Assessment Review","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Assembly of First Nations","funders":"","keywords":"Geographic information system; Geography; Environmental planning; Causal analysis; Environmental resource management; Regional science; Cartography; Business; Environmental science; Risk analysis (engineering)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028598,0.0001731174,0.0003025979,0.0001411486,0.0003366725,0.0002086554,0.0001354086,0.00002932889,0.0000944292],"category_scores_gemma":[0.00003159032,0.0001000286,0.00009302471,0.0008546168,0.00008841879,0.00188703,0.00006654402,0.0002163635,0.000007056633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009629903,"about_ca_system_score_gemma":0.0002970891,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8865312,"about_ca_topic_score_gemma":0.675298,"domain_scores_codex":[0.9982901,0.0002829104,0.0003679516,0.0001491138,0.0006417062,0.0002682474],"domain_scores_gemma":[0.9990761,0.0002840145,0.0003991542,0.0001504605,0.00001025846,0.00008004714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001617615,0.000009825962,0.9739211,0.0005728469,0.0008264631,0.000006378698,0.005297264,0.0000221498,0.000006981344,0.00009567574,0.0003666581,0.01887309],"study_design_scores_gemma":[0.0001202471,0.00001918597,0.9512351,0.0007666833,0.0004990919,0.000002622141,0.01513823,0.0002697397,4.886991e-7,0.000006305939,0.03177752,0.0001648329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9141928,0.07866526,0.0003878593,0.003284001,0.0001758425,0.001741466,0.00006908012,0.00003965308,0.001444058],"genre_scores_gemma":[0.9661357,0.03306763,0.00008695928,0.0004535348,0.00002640964,0.0001103936,0.0001000219,0.000004297909,0.00001499431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2112332,"threshold_uncertainty_score":0.4079048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009114482954020998,"score_gpt":0.3031389382680905,"score_spread":0.2940244553140695,"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."}}