{"id":"W7114599384","doi":"","title":"Time‐lapse cameras bridge the gap between remote sensing and in situ observations of tundra phenology","year":2025,"lang":"en","type":"other","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tundra; Phenology; Arctic; Climate change; Ecosystem; Vegetation (pathology); Satellite; Bay","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":[0.0004591492,0.000369603,0.0002111049,0.0007807062,0.0003109717,0.0008452007,0.000362371,0.0002942866,0.001175274],"category_scores_gemma":[0.001145744,0.0001721989,0.0001644901,0.0008225008,0.0002867349,0.0007397612,0.0004407077,0.0003800725,0.0002073528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005763716,"about_ca_system_score_gemma":0.0007206454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06596276,"about_ca_topic_score_gemma":0.2348193,"domain_scores_codex":[0.9997227,0.0000385151,0.00001513065,0.00007745011,0.00009852403,0.00004764133],"domain_scores_gemma":[0.9991929,0.000189792,0.000159747,0.00008286221,0.0003256158,0.00004902958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003506063,0.0001690447,0.4091768,0.0009951752,0.0002971173,0.0003460079,0.002224049,0.004567322,0.2817256,0.001058144,0.003601325,0.2954888],"study_design_scores_gemma":[0.00001283825,0.0001102251,0.9582482,0.0001360905,0.000119961,0.0003821518,0.001791734,0.007900705,0.01762034,0.0003220407,0.01332405,0.00003166667],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474783,0.002883132,0.03735147,0.0002562444,0.00009608635,0.0001236532,0.003039998,0.0003036832,0.008467441],"genre_scores_gemma":[0.946497,0.001579767,0.04881894,0.0001921731,0.00004705202,0.00006629559,0.001518502,0.00005204971,0.001228297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06596276,"threshold_uncertainty_score":0.1311576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05430389159426879,"score_gpt":0.2790394966915017,"score_spread":0.2247356050972329,"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."}}