{"id":"W89773641","doi":"","title":"Characterizing the tundra taiga interface using Radarsat-2 (Mealy Mountains, Labrador)","year":2012,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tundra; Remote sensing; Normalized Difference Vegetation Index; Taiga; Vegetation (pathology); Synthetic aperture radar; Environmental science; Earth observation; Satellite; Land cover; Multispectral image; Geography; Climate change; Arctic; Geology; Land use; Forestry; Ecology","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001334188,0.0006141819,0.0007149287,0.001153262,0.0037858,0.0004932024,0.002291716,0.000817942,0.001991561],"category_scores_gemma":[0.0001033254,0.0006019049,0.0004063198,0.001471018,0.0005503579,0.001556623,0.0002736115,0.002132893,0.0003454109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005951839,"about_ca_system_score_gemma":0.001164862,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08439316,"about_ca_topic_score_gemma":0.04968171,"domain_scores_codex":[0.9938871,0.001708445,0.0003855055,0.0009890317,0.001586301,0.001443654],"domain_scores_gemma":[0.9967672,0.0006816359,0.0004420014,0.0008673947,0.0006410041,0.0006007402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.1072025,0.001976775,0.157185,0.004866294,0.00612337,0.02797026,0.1484327,0.001431572,0.4339482,0.004418951,0.06308996,0.04335436],"study_design_scores_gemma":[0.002616036,0.0004752543,0.02479738,0.000438207,0.000673524,0.00008212424,0.06298123,0.0006319145,0.007091523,0.00004616047,0.8986527,0.00151397],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.913474,0.0005084002,0.0000103888,0.0001436784,0.02487176,0.0009539274,0.00111308,0.0001255441,0.05879924],"genre_scores_gemma":[0.8378378,0.001297856,0.00008994121,0.00003820296,0.02242766,1.897975e-7,0.007136398,0.0000808375,0.1310912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8355627,"threshold_uncertainty_score":0.9996432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05633435114488736,"score_gpt":0.2778694757070714,"score_spread":0.221535124562184,"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."}}