{"id":"W4415546166","doi":"10.1016/j.geomat.2025.100080","title":"Machine learning applied to remote sensing in the context of intertidal zone mapping: A literature review","year":2025,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Intertidal zone; Generalizability theory; Context (archaeology); Hyperspectral imaging; Lidar; Random forest; Key (lock); Digital elevation model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003725527,0.00008156675,0.0001918327,0.00008173114,0.00004180125,0.00002815054,0.000143712,0.00002426281,0.00008128776],"category_scores_gemma":[0.0001196616,0.00005207162,0.00003616163,0.0005289572,0.00002081228,0.00002472551,0.00003925968,0.0001775231,0.00002304803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002158412,"about_ca_system_score_gemma":0.00001447305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008756247,"about_ca_topic_score_gemma":0.004414678,"domain_scores_codex":[0.9993416,0.0000676168,0.0002329693,0.0001156838,0.0001036688,0.0001384753],"domain_scores_gemma":[0.9996266,0.0001358692,0.000045262,0.0001456169,0.00002139662,0.0000251964],"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":[0.00002412905,0.000005267388,0.00148886,0.0008829083,0.000009117197,0.00001391002,0.00154827,0.00009901232,0.00003626044,0.0002533602,0.0003000551,0.9953389],"study_design_scores_gemma":[0.001294304,0.0003255578,0.1000643,0.02756246,0.0001120862,0.0001679158,0.004544072,0.6065103,0.00005735842,0.007470488,0.2511685,0.0007227332],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5024842,0.04270446,0.06037106,0.03871832,0.0007642076,0.004240429,0.000171013,0.0002110015,0.3503353],"genre_scores_gemma":[0.9919397,0.0006654237,0.002922247,0.003623739,0.0000137473,2.425396e-7,0.00009410697,0.000001606929,0.0007391897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9946161,"threshold_uncertainty_score":0.2463494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007203075735222509,"score_gpt":0.2082121698438792,"score_spread":0.2010090941086567,"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."}}