{"id":"W4393612657","doi":"10.5281/zenodo.10048769","title":"Evergreen needleleaf forest pigment, MONI-PAM, eddy-covariance, and tower-scale remote sensing data across four different sites","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Eddy covariance; Evergreen; Scale (ratio); Environmental science; Tower; Evergreen forest; Covariance; Remote sensing; Atmospheric sciences; Geography; Mathematics; Ecology; Statistics; Geology; Biology; Ecosystem; Cartography","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":[0.0003134078,0.0002729225,0.0001989893,0.0009621278,0.000493272,0.0003632855,0.0004325348,0.0001690043,0.001227202],"category_scores_gemma":[0.0003094869,0.0001508666,0.0001544441,0.001569782,0.0001300409,0.0002162816,0.0003295156,0.0001916477,0.0003974148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007140196,"about_ca_system_score_gemma":0.0005225223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2046325,"about_ca_topic_score_gemma":0.4298142,"domain_scores_codex":[0.999821,0.00001618062,0.00001124566,0.00005667075,0.00005823205,0.00003665452],"domain_scores_gemma":[0.9995485,0.00004088507,0.00006556499,0.00004742751,0.0001968677,0.0001007803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004055079,0.0002037461,0.9339116,0.000103107,0.0001688578,0.0001382983,0.0006572457,0.002116306,0.01593962,0.0001519141,0.006786563,0.03941726],"study_design_scores_gemma":[0.00001952085,0.00001589724,0.993449,0.000005678882,0.00002805202,0.0000406673,0.0002236102,0.0004842463,0.001236461,0.00001679008,0.004471424,0.000008754835],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9704663,0.00009878662,0.0003719141,0.00002775886,0.000005448597,0.00004068444,0.02507065,0.00005752959,0.003860906],"genre_scores_gemma":[0.9145431,0.0001950413,0.004120706,0.00006078646,0.000008894245,0.0001747715,0.07695432,0.00003064343,0.003911681],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.2046325,"threshold_uncertainty_score":0.4068829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06548981450580608,"score_gpt":0.2760592982555048,"score_spread":0.2105694837496987,"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."}}