{"id":"W4411968843","doi":"10.1101/2025.06.27.661894","title":"Monitoring tropical forests with light drones: ensuring spatial and temporal consistency in stereophotogrammetric products","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Agence Nationale de la Recherche; Biodiversa+","keywords":"Consistency (knowledge bases); Drone; Tropical forest; Geography; Environmental science; Remote sensing; Computer science; Ecology; Artificial intelligence; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008381263,0.0002063046,0.0002288872,0.0009926112,0.0002329155,0.0005911731,0.0004531961,0.0002595037,0.0006119792],"category_scores_gemma":[0.002246027,0.0001311493,0.0001248165,0.0008766815,0.0002295531,0.0005382164,0.0005818046,0.0002219179,0.0002851045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520603,"about_ca_system_score_gemma":0.0003909878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01045759,"about_ca_topic_score_gemma":0.01864964,"domain_scores_codex":[0.9993806,0.0001176643,0.00002225886,0.0001855949,0.0002088517,0.00008496535],"domain_scores_gemma":[0.9989423,0.0001875907,0.0002328347,0.000215615,0.0003616681,0.00006007143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001070325,0.0002447997,0.1929658,0.0002363132,0.0001399597,0.0004247396,0.0008183689,0.0980114,0.1961351,0.00165792,0.003449108,0.5048463],"study_design_scores_gemma":[0.00007265071,0.0003689829,0.3382358,0.0000522674,0.00007703669,0.0004024347,0.0007991834,0.5995089,0.05263644,0.001150244,0.006637404,0.00005868063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.89981,0.0001960262,0.09567533,0.0001026529,0.00002352583,0.00006707788,0.0008621997,0.000888687,0.002374506],"genre_scores_gemma":[0.9774703,0.00002838974,0.02187254,0.00001353244,0.000009521514,0.00001345107,0.0003679578,0.00003304341,0.000191179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01045759,"threshold_uncertainty_score":0.0207935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01067734160904549,"score_gpt":0.2094300560178239,"score_spread":0.1987527144087784,"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."}}