{"id":"W2051184205","doi":"10.14214/sf.618","title":"Predictions of forest inventory cover type proportions using Landsat TM","year":2000,"lang":"en","type":"article","venue":"Silva Fennica","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Forest inventory; Forest cover; Environmental science; Scale (ratio); Land cover; Remote sensing; Statistics; Physical geography; Cartography; Mathematics; Geography; Forest management; Ecology; Land use","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":["insufficient_payload"],"category_scores_codex":[0.00006478137,0.00006836891,0.00008413829,0.00002007385,0.0001408749,0.000009173395,0.00009942982,0.00004914507,0.01077437],"category_scores_gemma":[0.00001864337,0.00006266438,0.00004651793,0.0002700624,0.0001806317,0.00007287332,0.00002998509,0.00007866581,0.0009006569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006177008,"about_ca_system_score_gemma":0.00002688866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004643758,"about_ca_topic_score_gemma":0.0001092775,"domain_scores_codex":[0.9993476,0.00002043235,0.0001735586,0.000165136,0.0001537603,0.0001394735],"domain_scores_gemma":[0.999555,0.00001772047,0.00005453403,0.0002940637,0.00001038053,0.00006829334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001809773,0.001469705,0.2686775,0.00004954021,0.0001511861,0.0000161165,0.002238276,0.2885752,0.05120198,0.0005716094,0.3456437,0.04122429],"study_design_scores_gemma":[0.0003589435,0.0001136818,0.2108636,0.00004308484,0.00008039914,0.00004437127,0.00007813951,0.05646788,0.0006206925,0.001003304,0.7300532,0.0002726067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8885772,0.0000229226,0.0002813847,0.0001394187,0.0000630572,0.0001782576,0.00003358514,0.00005520156,0.110649],"genre_scores_gemma":[0.9913571,0.0000222067,0.001539183,0.00005492478,0.0000448601,0.000002063561,0.00003566458,0.00001105557,0.006932924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3844096,"threshold_uncertainty_score":0.9998773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763332748311633,"score_gpt":0.2464097048980171,"score_spread":0.2287763774149008,"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."}}