{"id":"W4408959150","doi":"10.3390/f16040592","title":"Visualization of Post-Fire Remote Sensing Using CiteSpace: A Bibliometric Analysis","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Visualization; Remote sensing; Environmental science; Computer science; Geography; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0003523779,0.0001032865,0.0002205808,0.01502373,0.00007393865,0.00003187926,0.0001211042,0.00007105693,0.0001156922],"category_scores_gemma":[0.0003213082,0.0001004217,0.0001029089,0.1553659,0.000050514,0.000153495,0.000120733,0.00004328877,0.00003533305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001687525,"about_ca_system_score_gemma":0.00001403693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005997082,"about_ca_topic_score_gemma":0.003758776,"domain_scores_codex":[0.9989359,0.00008868157,0.0002396268,0.0002378161,0.0003086355,0.0001893413],"domain_scores_gemma":[0.9993731,0.0001111816,0.0001487906,0.0002883397,0.00003369358,0.00004490495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001785961,0.00002827972,0.8869053,0.000076902,0.0001580542,0.000008059917,0.000132464,0.01367157,0.02123489,0.00002111544,0.0002930068,0.07745246],"study_design_scores_gemma":[0.00008329796,0.00001962401,0.4841548,0.000034448,0.0001084712,0.000001452472,0.000006827357,0.5138855,0.001519634,0.00003975996,0.00008808476,0.00005817047],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.941326,0.00008199328,0.0574288,0.00002967534,0.000114479,0.0001730352,0.000004049746,0.00003587841,0.0008060723],"genre_scores_gemma":[0.9973095,0.000004393448,0.002377688,0.00004708004,0.0000111363,1.919512e-7,0.00001076747,0.000009990075,0.0002292355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5002139,"threshold_uncertainty_score":0.9961401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010196161197852,"score_gpt":0.2842381822484416,"score_spread":0.2740420210505896,"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."}}