{"id":"W4402837701","doi":"10.1101/2024.09.23.614649","title":"Invisible people: Exploring how well remote-sensed datasets reveal the distribution of forest-proximate populations","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Government of the United Kingdom","keywords":"Geography; Distribution (mathematics); Proximate; Remote sensing; Biology; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004801634,0.0006175372,0.0005345005,0.004128965,0.0004420726,0.002252307,0.000902227,0.00100566,0.001173804],"category_scores_gemma":[0.01751864,0.0002403925,0.0006015609,0.002907104,0.0007184976,0.002326733,0.0017075,0.0006785529,0.0006666225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003833064,"about_ca_system_score_gemma":0.0002926392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008909623,"about_ca_topic_score_gemma":0.01406257,"domain_scores_codex":[0.9973132,0.001217309,0.0001405215,0.0007053697,0.0003862992,0.0002373553],"domain_scores_gemma":[0.9903047,0.006319266,0.001034375,0.001388887,0.0006507917,0.0003018756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005587073,0.0003634729,0.8899162,0.000534665,0.0008230774,0.0002761082,0.001753502,0.02151171,0.003005034,0.002264872,0.01432166,0.06467106],"study_design_scores_gemma":[0.0001101904,0.0001963126,0.8276652,0.0003130473,0.000231376,0.0003923285,0.005183304,0.1324313,0.002227413,0.008797972,0.02233048,0.0001212679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959099,0.001090055,0.01059052,0.001100576,0.0001179671,0.00005504191,0.02310924,0.0005783926,0.004259207],"genre_scores_gemma":[0.9536979,0.0002196388,0.01292988,0.000184023,0.0001194431,0.000053918,0.03227169,0.00009309223,0.0004303958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008909623,"threshold_uncertainty_score":0.02539378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03012254904314065,"score_gpt":0.225602786531835,"score_spread":0.1954802374886943,"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."}}