{"id":"W4292074775","doi":"10.1016/j.eiar.2022.106851","title":"A geospatial framework for the assessment and monitoring of environmental impacts of agriculture","year":2022,"lang":"en","type":"article","venue":"Environmental Impact Assessment Review","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Geospatial analysis; Agriculture; Environmental resource management; Environmental planning; Environmental impact assessment; Food security; Wetland; Watershed; Business; Environmental monitoring; Environmental science; Geography; Remote sensing; Computer science; Environmental engineering; Ecology","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":[0.0009920287,0.0002935416,0.00057804,0.00002798775,0.0003931955,0.00001727939,0.0003956766,0.00005408672,0.006847421],"category_scores_gemma":[0.0000229524,0.0001843144,0.0004402221,0.0001912522,0.0002651138,0.0001755573,0.000623058,0.0003395721,0.000003796106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007283295,"about_ca_system_score_gemma":0.00002297741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002018783,"about_ca_topic_score_gemma":0.000005388344,"domain_scores_codex":[0.9975639,0.0002421849,0.0006039385,0.0003855097,0.0008594183,0.0003450564],"domain_scores_gemma":[0.9984661,0.0004184972,0.0004784047,0.0004923494,0.000001685174,0.0001429546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000200098,0.0006006662,0.9631914,0.0002024118,0.0002244092,0.000001480259,0.0002843827,0.0007077495,0.007702183,0.0000355113,0.0003153442,0.0267144],"study_design_scores_gemma":[0.0003651069,0.0005305288,0.9939841,0.0001271971,0.0004535706,0.00001545164,0.001006835,0.000351931,0.0002872706,0.0002135216,0.002436419,0.0002280641],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760561,0.02013297,0.001125157,0.0004909495,0.0001207953,0.001458169,0.0004793614,0.00001150593,0.0001250117],"genre_scores_gemma":[0.9763098,0.01967621,0.003441392,0.0001268336,0.00003905256,0.0002819845,0.00006653414,0.00001950325,0.00003863727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03079265,"threshold_uncertainty_score":0.9940605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151231650818169,"score_gpt":0.315495696485927,"score_spread":0.3039833799777453,"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."}}