{"id":"W4316362365","doi":"10.5194/isprs-annals-x-4-w1-2022-669-2023","title":"IDENTIFYING SUITABLE LOCATIONS FOR MANGROVE PLANTATION USING GEOSPATIAL INFORMATION SYSTEM AND REMOTE SENSING","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Mangrove; Threatened species; Geospatial analysis; Environmental science; Environmental resource management; Computer science; Remote sensing; Geography; Ecology; Habitat","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":[],"consensus_categories":[],"category_scores_codex":[0.001762756,0.0001442936,0.0002122668,0.0002656344,0.001048522,0.0003615075,0.0001413266,0.00008547349,0.000004027113],"category_scores_gemma":[0.0003703267,0.0001100769,0.0000953503,0.001061264,0.0003318942,0.001006504,0.0001561706,0.0000861224,0.000009035583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004002713,"about_ca_system_score_gemma":0.00002958304,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2653185,"about_ca_topic_score_gemma":0.01450019,"domain_scores_codex":[0.9983556,0.00008850448,0.0005609589,0.0001719724,0.0005031294,0.0003198408],"domain_scores_gemma":[0.9990016,0.0001880417,0.0004302116,0.0001890749,0.0001113464,0.0000797239],"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":[0.0000243858,0.00000187746,0.0005821723,0.0001141472,0.00001614421,1.383632e-7,0.003115646,0.008436864,0.0003694227,0.000005082271,0.0001137692,0.9872203],"study_design_scores_gemma":[0.0001815878,0.00003645748,0.006088077,0.0001039338,0.00003641735,0.00001789632,0.006184695,0.9834287,0.002776058,0.0005596931,0.0004470571,0.0001394925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4293547,0.000009506586,0.5693405,0.0003868403,0.0002200821,0.0002877726,0.00002572855,0.00004613604,0.0003286845],"genre_scores_gemma":[0.9948715,0.00003304421,0.00479059,0.0002247523,0.0000288011,1.37921e-7,0.00002907677,0.000004292742,0.00001780824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9870809,"threshold_uncertainty_score":0.8091445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05649315636187153,"score_gpt":0.3055374756817532,"score_spread":0.2490443193198817,"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."}}