{"id":"W4415479072","doi":"10.1007/s44288-025-00278-4","title":"Forest losses are associated with oil and gas seismic cutlines in Northeastern British Columbia, Canada","year":2025,"lang":"en","type":"article","venue":"Discover Geoscience","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Elevation (ballistics); Forest cover; Kernel density estimation; Variance (accounting); Land cover; Geographic information system; Spatial analysis; Fossil fuel","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":[],"consensus_categories":[],"category_scores_codex":[0.00008656616,0.00006717843,0.0001023088,0.00001144191,0.0003226899,0.0004072883,0.0001565495,0.00002318998,0.00001344731],"category_scores_gemma":[0.00005737262,0.00008061456,0.000009720595,0.0005166577,0.0003087192,0.0001509578,0.00008804064,0.00007273492,0.000002938107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419083,"about_ca_system_score_gemma":0.0001436033,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9829507,"about_ca_topic_score_gemma":0.9997014,"domain_scores_codex":[0.9990886,0.00001963454,0.0001141925,0.0003264362,0.0002208616,0.0002302518],"domain_scores_gemma":[0.9996826,0.00004234312,0.00005367216,0.0001576893,0.00000957997,0.00005410231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001177928,0.0000294873,0.984643,0.000003774983,0.000001649996,0.00001840912,0.00003073265,0.00122874,0.00002215295,0.000001556483,0.0007502357,0.01326911],"study_design_scores_gemma":[0.0001378279,0.000008199953,0.9874095,0.0001082905,0.000004754976,0.00001092331,0.0001796904,0.008432599,0.000006834044,0.0001436377,0.003451151,0.0001065672],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945035,0.00002674129,0.0001597558,0.0007053282,0.00004512996,0.00005179077,0.00002939191,0.00001292105,0.004465442],"genre_scores_gemma":[0.9826389,0.00001787735,0.00005515247,0.000554359,0.000004338071,0.000003568288,0.000006728712,0.000004453797,0.01671462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0167507,"threshold_uncertainty_score":0.3927491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003685732327022199,"score_gpt":0.1830829326895035,"score_spread":0.1793972003624813,"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."}}