{"id":"W4383722633","doi":"10.1002/2688-8319.12254","title":"Applying remote sensing for large‐landscape problems: Inventorying and tracking habitat recovery for a broadly distributed Species At Risk","year":2023,"lang":"en","type":"article","venue":"Ecological Solutions and Evidence","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Alberta Biodiversity Monitoring Institute; University of Lethbridge; University of Alberta","funders":"","keywords":"Lidar; Habitat; Vegetation (pathology); Environmental science; Woodland caribou; Threatened species; Remote sensing; Biodiversity; Restoration ecology; Ecology; Disturbance (geology); Geography; Physical geography; Geology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009555102,0.0001336585,0.0001805062,0.00003610655,0.001764864,0.0001027171,0.00007364281,0.00009632103,0.0000302825],"category_scores_gemma":[0.001077986,0.0001144046,0.00007630826,0.0002884358,0.0001524654,0.0001790805,0.0002270076,0.0001067054,0.00003952435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001325532,"about_ca_system_score_gemma":0.000008325951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006491284,"about_ca_topic_score_gemma":0.0006344282,"domain_scores_codex":[0.9986625,0.00006044535,0.0002354658,0.0004544093,0.000111264,0.0004759052],"domain_scores_gemma":[0.998476,0.001122773,0.0001167338,0.0001481543,0.00002173652,0.0001145877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003050584,0.0001885334,0.1178743,0.0004942822,0.0001223927,0.00001311809,0.001827245,0.02304691,0.04119578,0.0008097891,0.04865472,0.7654679],"study_design_scores_gemma":[0.0006364195,0.0002655087,0.3574091,0.0002402666,0.00009296387,0.00003377007,0.0005895173,0.5672508,0.00009059686,0.01713534,0.0558367,0.0004189748],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7616916,0.0003986988,0.2341071,0.001609041,0.0001010331,0.001620733,0.00006754389,0.0001782058,0.0002260589],"genre_scores_gemma":[0.9838697,0.0008013539,0.01466698,0.00009882099,0.00006442852,0.00003843636,0.0000439712,0.0000123165,0.0004040012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7650489,"threshold_uncertainty_score":0.9995347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06129500913509121,"score_gpt":0.2719569869670841,"score_spread":0.2106619778319929,"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."}}