{"id":"W2981846308","doi":"10.4095/219782","title":"Sensitivity of Landscape Indices to Classification Accuracy","year":2001,"lang":"en","type":"report","venue":"","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Sensitivity (control systems); Computer science; Geography; Statistics; Cartography; Artificial intelligence; Mathematics; Engineering","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":[0.001130803,0.0001810604,0.0003683706,0.0002093324,0.00006231086,0.00004732392,0.0001019162,0.0002327615,0.001495971],"category_scores_gemma":[0.0004966198,0.00012716,0.00009315263,0.0002791364,0.000026879,0.00007635404,0.000008599069,0.0001949315,0.0002794416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005295033,"about_ca_system_score_gemma":0.0003862715,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03557188,"about_ca_topic_score_gemma":0.03027554,"domain_scores_codex":[0.9983692,0.0001305721,0.0003317669,0.000317387,0.0006306704,0.0002204681],"domain_scores_gemma":[0.9986265,0.0004238848,0.0002798458,0.000323689,0.0002060419,0.0001400581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000271039,0.00001559982,0.3708906,0.00007675747,0.00003884992,0.00004133386,0.0001040917,0.0002863416,0.00003045897,0.000001502665,0.03948446,0.5890029],"study_design_scores_gemma":[0.0000551736,0.00005116442,0.7909513,0.00007634348,0.00003099026,0.00008841805,0.0000849208,0.002650118,0.00004220053,0.00001215448,0.2057739,0.0001833972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3135635,0.0002811486,0.000197222,0.0002395697,0.0008337048,0.0001972566,0.0001388319,0.0000655394,0.6844832],"genre_scores_gemma":[0.9891915,0.001472097,0.000498213,0.00009753065,0.0004717718,6.62253e-8,0.000732514,0.00000575113,0.007530563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6769527,"threshold_uncertainty_score":0.9994168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05816205347329144,"score_gpt":0.2889128933335078,"score_spread":0.2307508398602163,"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."}}