{"id":"W6975424964","doi":"10.60510/awfwi02618","title":"IGSN AWFWI02618 (EN22040-T09): Individual Sample (Biology, plant - Larix laricina) from Ogilvie Mountains (Nahoni Range), Yukon, CA","year":2024,"lang":"en","type":"other","venue":"GFZ IGSN Sample Catalogue","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Sample (material); Hydrology (agriculture); Vegetation (pathology); Productivity; Precipitation","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":[0.000881216,0.001731057,0.001403943,0.008360031,0.00174288,0.002002093,0.002511468,0.001377627,0.1707287],"category_scores_gemma":[0.003370954,0.000884573,0.0006865747,0.01891847,0.0005428094,0.001050712,0.002199819,0.0007500402,0.153767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002189,"about_ca_system_score_gemma":0.007176991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.213231,"about_ca_topic_score_gemma":0.305244,"domain_scores_codex":[0.9989395,0.00004825618,0.0001330754,0.0003100763,0.0002486244,0.000320423],"domain_scores_gemma":[0.9976811,0.0001909914,0.0002351996,0.0005676411,0.001049608,0.0002753256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001760857,0.00003026713,0.005702913,0.001126217,0.00005914477,0.0001098229,0.0004773545,0.0001537916,0.001255794,0.001175634,0.9761896,0.01354335],"study_design_scores_gemma":[0.00005444538,0.00001238717,0.03110446,0.0002269737,0.00006166034,0.0001000747,0.0003649394,0.00006118319,0.0005270707,0.0005216411,0.96694,0.00002509655],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000882472,0.00005002315,0.0002297182,0.0000199417,0.00002184518,0.00002674662,0.9932869,0.0003839112,0.005098419],"genre_scores_gemma":[0.001228232,0.00006343055,0.0006029519,0.00002910057,0.000004874034,0.00009898916,0.9926702,0.00045376,0.004848545],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.786769,"threshold_uncertainty_score":0.5711443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03644028532335618,"score_gpt":0.2812226276927139,"score_spread":0.2447823423693577,"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."}}