{"id":"W4413835157","doi":"10.20944/preprints202508.2105.v1","title":"Separating Crested Wheatgrass Using Field Hyperspectral Data in the Native Prairie of Southwestern Saskatchewan","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Geography; Field (mathematics); Forestry; Agroforestry; Environmental science; Remote sensing; Agronomy; Biology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003635977,0.0002832081,0.0001852388,0.0005887513,0.0005101242,0.0005087337,0.0004023456,0.0002151087,0.0006790691],"category_scores_gemma":[0.0004780593,0.0002004279,0.000202539,0.0009775204,0.000384356,0.0003299954,0.0004195295,0.0002206575,0.0001765614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002066582,"about_ca_system_score_gemma":0.002221428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7086937,"about_ca_topic_score_gemma":0.9260826,"domain_scores_codex":[0.9998177,0.00003063802,0.0000133715,0.00007009983,0.00002818733,0.00004008719],"domain_scores_gemma":[0.9995752,0.00006169754,0.00005399625,0.00003976877,0.0001919503,0.00007744977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001850256,0.000128853,0.9594713,0.00004081865,0.0001292746,0.0002440466,0.001060135,0.0009006077,0.01798304,0.00008900627,0.0003751611,0.01939266],"study_design_scores_gemma":[0.000006941663,0.00001753915,0.996444,0.000009768833,0.00001640525,0.00002881237,0.00113134,0.00160039,0.0004005092,0.0000271992,0.0003079216,0.000009228875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999244,0.00002484105,0.0001261885,0.00001656098,0.000001182727,0.0000107489,0.0002659998,0.000004924509,0.0003056887],"genre_scores_gemma":[0.9979439,0.00006816148,0.0007442086,0.00004048217,0.000001040401,0.0000152176,0.0006484483,0.000004453457,0.0005339441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2913063,"threshold_uncertainty_score":0.5860436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.13833137914872,"score_gpt":0.3698320618462593,"score_spread":0.2315006826975392,"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."}}