{"id":"W1966921058","doi":"10.3390/s141121117","title":"Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Ecosystem health; Ecosystem services; Environmental resource management; Ecosystem; Hyperspectral imaging; Environmental science; Computer science; Lidar; Resilience (materials science); Radar; Geography; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005852337,0.0005455827,0.001885094,0.00009266631,0.0001230276,0.00002310327,0.0001980251,0.0004408871,0.00003862895],"category_scores_gemma":[0.00004057835,0.0003931412,0.0002906107,0.00016428,0.0001963604,0.00004742996,0.0001748968,0.0004753406,0.00005763353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000420237,"about_ca_system_score_gemma":0.00005490536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001793996,"about_ca_topic_score_gemma":0.0003194123,"domain_scores_codex":[0.9969939,0.0007314268,0.0006954856,0.0007399991,0.0004200542,0.0004191101],"domain_scores_gemma":[0.9981148,0.0001495263,0.000860577,0.0006129431,0.00002445689,0.0002377295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000115303,0.000006265679,3.319911e-8,0.00579534,0.00004507539,0.00001906757,0.00199094,0.000003193733,3.984364e-7,0.00007250143,0.0008627569,0.9912032],"study_design_scores_gemma":[0.00006364554,0.00005673262,0.000009399166,0.005887237,0.0001063756,0.0006099533,0.003300427,0.0001614475,3.121396e-7,0.00002942637,0.9893985,0.0003765495],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007257101,0.9848642,0.000006683044,0.0008251185,0.0003605709,0.0005798237,0.00002712451,0.00007050134,0.01319345],"genre_scores_gemma":[0.00001766731,0.995423,0.003088265,0.00003318042,0.0006381621,1.063686e-7,0.00002488137,0.00006626036,0.0007084418],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9908267,"threshold_uncertainty_score":0.9998521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06403514250655645,"score_gpt":0.2836487556206685,"score_spread":0.219613613114112,"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."}}