{"id":"W3036795991","doi":"10.1007/s10980-020-01048-y","title":"Six key steps for functional landscape analyses of habitat change","year":2020,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Landscape ecology; Ecology; Conceptual framework; Perspective (graphical); Landscape assessment; Raster graphics; Environmental resource management; Habitat; Process (computing); Geography; Landscape design; Computer science; Sociology; Environmental science; Biology; Artificial intelligence","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":[0.0001071529,0.0001036502,0.0002413376,0.00003148236,0.0001165421,0.000003812059,0.0001129289,0.00009886295,0.002363505],"category_scores_gemma":[0.0001201367,0.00008946668,0.00008029399,0.0001505458,0.0001095449,0.00008415685,0.0001074402,0.00007133684,0.0002073177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001421802,"about_ca_system_score_gemma":0.000008235698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007562854,"about_ca_topic_score_gemma":0.001758933,"domain_scores_codex":[0.9992245,0.00004199953,0.000201682,0.0002427473,0.00008068335,0.0002084106],"domain_scores_gemma":[0.9994238,0.0002990228,0.000111645,0.00008259134,0.00002069544,0.00006227302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009172109,0.00005557175,0.9822801,0.00002113236,0.00006886615,0.000001965101,0.0004800012,0.00124665,0.0005793762,0.0003791443,0.01462323,0.0001722071],"study_design_scores_gemma":[0.0008231329,0.0004113556,0.979653,0.00000140099,0.00005754166,0.000002588097,0.0001910684,0.01546842,0.0001076475,0.0003799014,0.002791778,0.0001121754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839002,0.0001702817,0.00122862,0.005151732,0.0004039115,0.0003809496,0.00004813193,0.00004510036,0.008671026],"genre_scores_gemma":[0.9970665,0.00002947855,0.000690465,0.001672858,0.0001315121,0.0001203098,0.00005605941,0.000008704634,0.0002241357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01422177,"threshold_uncertainty_score":0.9985484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05700570907103678,"score_gpt":0.2803453537371198,"score_spread":0.2233396446660831,"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."}}