{"id":"W3176233530","doi":"10.1002/esp.5179","title":"Remote sensing of large wood in high‐resolution satellite imagery: Design of an automated classification work‐flow for multiple wood deposit types","year":2021,"lang":"en","type":"article","venue":"Earth Surface Processes and Landforms","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Support vector machine; Satellite imagery; Remote sensing; Contextual image classification; Pixel; Computer science; Geology; Artificial intelligence; Environmental science; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001167337,0.0005516349,0.0005170982,0.001372122,0.0006931029,0.0009171168,0.001100963,0.0007139576,0.001236092],"category_scores_gemma":[0.001459946,0.0005358313,0.0005283681,0.0005220367,0.0006773534,0.0008707166,0.0006096608,0.0003462447,0.0005137684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000910004,"about_ca_system_score_gemma":0.0009925006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008462279,"about_ca_topic_score_gemma":0.006060928,"domain_scores_codex":[0.999491,0.00007793743,0.00003086243,0.0001993644,0.0001224842,0.00007836733],"domain_scores_gemma":[0.9991021,0.0002093114,0.0001080523,0.00006081454,0.0004554996,0.00006425844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001286981,0.002298426,0.04782873,0.0001646895,0.0001291694,0.0002269437,0.0005287938,0.2028312,0.1617302,0.001562133,0.001825184,0.5795876],"study_design_scores_gemma":[0.00001767047,0.0001460007,0.006718012,0.000005652145,0.00002087519,0.00001975042,0.00006195445,0.9791721,0.0133581,0.0001990829,0.0002696476,0.0000111791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4058529,0.000062592,0.5903085,0.0001368434,0.00002295812,0.0007447647,0.00009237248,0.001407731,0.001371324],"genre_scores_gemma":[0.7204047,0.00004018697,0.276898,0.00007869773,0.00002565866,0.0007194942,0.0001776152,0.00005443045,0.001601294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008462279,"threshold_uncertainty_score":0.01682603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531543147006876,"score_gpt":0.2446077473642418,"score_spread":0.2292923158941731,"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."}}