{"id":"W2065910099","doi":"10.1080/01431160903349040","title":"Employing ground-based spectroscopy for tree-species differentiation in the Gulf Islands National Park Reserve","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada; University of British Columbia","funders":"University of British Columbia; Parks Canada","keywords":"Hyperspectral imaging; Imaging spectrometer; Remote sensing; Spectrometer; Wavelength; Environmental science; Scale (ratio); Spectral signature; Reflectivity; Linear discriminant analysis; Vegetation (pathology); Geography; Cartography; Mathematics; Statistics; Optics; Physics","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":[0.0003378126,0.0001524661,0.0001159996,0.0006248662,0.0004964106,0.0003631022,0.0002911759,0.0001151426,0.0003403833],"category_scores_gemma":[0.0005822876,0.00008199819,0.00008048259,0.0005224623,0.0001917523,0.0001887474,0.0002038263,0.0001678156,0.00005969725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072159,"about_ca_system_score_gemma":0.001205986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4923399,"about_ca_topic_score_gemma":0.8264679,"domain_scores_codex":[0.9998729,0.00002758333,0.000004968028,0.00002458394,0.00005380551,0.00001612925],"domain_scores_gemma":[0.9998234,0.00003889094,0.00003007487,0.00001270405,0.00007020574,0.00002481547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001611953,0.0001302405,0.8787724,0.00006107837,0.00005195255,0.0002025843,0.001574865,0.001883573,0.04268688,0.0001769215,0.0004597599,0.07383849],"study_design_scores_gemma":[0.000008607019,0.00003113362,0.9861813,0.00001020459,0.00001672186,0.0000695264,0.001247038,0.006208169,0.00522005,0.00005283477,0.0009474009,0.000007099007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991463,0.00004063575,0.0002720928,0.00001334765,7.778605e-7,0.000009449405,0.00006187614,0.000007959155,0.0004474596],"genre_scores_gemma":[0.9936703,0.00007048327,0.005546477,0.000009577961,8.092476e-7,0.0000078555,0.0002868416,0.000004785189,0.0004027238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4923399,"threshold_uncertainty_score":0.9789484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742635094263692,"score_gpt":0.2985831266195442,"score_spread":0.2611567756769073,"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."}}