{"id":"W2034102594","doi":"10.1191/0309133305pp455ra","title":"Remote sensing for large-area habitat mapping","year":2005,"lang":"en","type":"article","venue":"Progress in Physical Geography Earth and Environment","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Saskatchewan; University of Calgary","funders":"","keywords":"Remote sensing; Habitat; Traverse; Geography; Wildlife; Resource (disambiguation); Environmental resource management; Computer science; Ecology; Data science; Environmental science; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0001701876,0.0001398387,0.000146882,0.00004205913,0.0001540054,0.00001815938,0.00006748077,0.00005747659,0.00006254187],"category_scores_gemma":[0.000006154036,0.0001325382,0.00007439649,0.0001026417,0.0002128046,0.0001446389,0.00009888494,0.0001112524,0.00004872244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001986173,"about_ca_system_score_gemma":0.000001753899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009681307,"about_ca_topic_score_gemma":0.00004900432,"domain_scores_codex":[0.9989598,0.00003318817,0.000149358,0.0003442972,0.0001397727,0.000373632],"domain_scores_gemma":[0.9996581,0.00005906833,0.00005089723,0.0001434229,0.000001542484,0.00008698956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002077669,0.0001020976,0.6328273,0.000005958659,0.000008242565,0.000001600086,0.0001803251,0.0001967002,0.00004204191,0.0000316048,0.0000538215,0.3665295],"study_design_scores_gemma":[0.0005686817,0.00008287412,0.9229814,0.00001348045,0.00001050614,0.000001970981,0.00003360163,0.05191113,0.0001039599,0.002022647,0.02209834,0.0001713908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942185,0.0002356868,0.003478629,0.001308562,0.00003031309,0.0003511745,0.000004964885,0.00002512177,0.000347037],"genre_scores_gemma":[0.9788557,0.00006250231,0.02047268,0.0004611945,0.0000712789,0.00001418252,0.00001107009,0.000009596968,0.00004181213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3663581,"threshold_uncertainty_score":0.540475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009101482689379993,"score_gpt":0.2121553500511368,"score_spread":0.2030538673617568,"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."}}