{"id":"W4293215627","doi":"10.3389/fevo.2022.911051","title":"Vegetation and vantage point influence visibility across diverse ecosystems: Implications for animal ecology","year":2022,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Idaho Space Grant Consortium; National Aeronautics and Space Administration; National Science Foundation","keywords":"Viewshed analysis; Ecology; Vegetation (pathology); Habitat; Ecosystem; Geography; Canopy; Visibility; Shrub; Ecotone; Environmental science; Remote sensing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007480107,0.00009027658,0.000153085,0.00005257164,0.0007817466,0.00000798915,0.0000967532,0.0001200858,0.00003426854],"category_scores_gemma":[0.0001196139,0.0001068133,0.00002172141,0.0001441792,0.0002733689,0.0003154133,0.000208857,0.0001479631,0.000005350266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005289984,"about_ca_system_score_gemma":0.00002328516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001130633,"about_ca_topic_score_gemma":0.00388142,"domain_scores_codex":[0.9988965,0.0001906635,0.0002286847,0.000370893,0.00004413015,0.0002691116],"domain_scores_gemma":[0.9995936,0.0001174352,0.0001157118,0.0001183444,0.00001189995,0.00004305562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001219173,0.00006785432,0.9949064,0.00001266578,0.000007659404,8.236615e-7,0.0004775083,0.001511112,0.0002141868,0.0005783316,0.001558918,0.0005426274],"study_design_scores_gemma":[0.000666292,0.0002955884,0.9772134,0.000001313833,0.00001345221,0.00001785585,0.0008839712,0.009349938,0.000003464298,0.01096349,0.000488314,0.000102851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962403,0.00007692209,0.001440221,0.001102391,0.0004438652,0.0005763125,0.00004138916,0.00002246801,0.00005612316],"genre_scores_gemma":[0.9978788,0.00001884931,0.001146798,0.0004164162,0.00001456397,0.0004501461,0.00003143387,0.000004936767,0.00003800707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01769293,"threshold_uncertainty_score":0.6012642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006897946872955429,"score_gpt":0.2373044590864757,"score_spread":0.2304065122135203,"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."}}