{"id":"W3152702852","doi":"10.1101/2021.04.11.439344","title":"Characterizing the effect of small-scale topographic variability on co-existing native and invasive species in a heterogeneous grassland using airborne hyperspectral remote sensing","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Invasive species; Hyperspectral imaging; Grassland; Remote sensing; Vegetation (pathology); Ecosystem; Biodiversity; Environmental science; Introduced species; Species distribution; Ecology; Geography; Habitat; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002638975,0.000249417,0.0001654802,0.0007683563,0.0004410424,0.0006377334,0.0002019079,0.0001531252,0.000377988],"category_scores_gemma":[0.0006051877,0.0001191131,0.0001681436,0.0006599548,0.0004563077,0.000308187,0.0002461038,0.0001047589,0.00006135676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008142053,"about_ca_system_score_gemma":0.0004501716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2143163,"about_ca_topic_score_gemma":0.4166686,"domain_scores_codex":[0.9998234,0.00001598645,0.000007533179,0.00006439173,0.00005115424,0.00003756595],"domain_scores_gemma":[0.9996709,0.00009958986,0.00007980215,0.00002730046,0.00008853804,0.0000338778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002077935,0.00007129904,0.8873818,0.00008719161,0.0001582179,0.0002906556,0.0004491123,0.008662995,0.07748295,0.00008683089,0.0002402886,0.02488088],"study_design_scores_gemma":[0.000002237328,0.00000879766,0.9917818,0.000002506188,0.00001515748,0.00003733485,0.0001730315,0.007070546,0.0008008323,0.00001562174,0.0000877423,0.000004295754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991147,0.00003428563,0.0003908006,0.000005146462,8.976808e-7,0.000005143454,0.00009371751,0.00001011858,0.0003451651],"genre_scores_gemma":[0.999238,0.00002264358,0.0005221015,0.000004400237,0.0000010771,0.000002267308,0.0001362305,0.000002205478,0.00007094738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2143163,"threshold_uncertainty_score":0.4261376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452017895127043,"score_gpt":0.2140109294331607,"score_spread":0.1994907504818903,"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."}}