{"id":"W4393542943","doi":"10.5281/zenodo.10048770","title":"Evergreen needleleaf forest pigment, MONI-PAM, eddy-covariance, and tower-scale remote sensing data across four different sites","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Eddy covariance; Evergreen; Environmental science; Scale (ratio); Tower; Evergreen forest; Remote sensing; Geography; Biology; Ecology; Cartography; Ecosystem","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005032304,0.0006747034,0.0007687305,0.0001581803,0.0002401558,0.001292465,0.001559132,0.0004703457,0.00006371044],"category_scores_gemma":[0.0002026868,0.0006898178,0.00005942352,0.0003557506,0.000124328,0.0005816637,0.002107019,0.0006848634,0.001126766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001941698,"about_ca_system_score_gemma":0.00005744829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009426245,"about_ca_topic_score_gemma":0.00752207,"domain_scores_codex":[0.9969274,0.00007534772,0.0006527568,0.001203681,0.000421298,0.0007194934],"domain_scores_gemma":[0.9961708,0.0002096441,0.0002433102,0.00308388,0.00008035709,0.0002119902],"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":[0.0000244965,0.00001412069,0.00001606619,0.0001339978,0.0001358032,0.0001145072,0.00008432682,0.0007291762,0.001772521,3.48104e-8,0.8913708,0.1056042],"study_design_scores_gemma":[0.0006349836,0.00003661076,0.001398947,0.0006194971,0.0001650083,0.00008788904,0.000143124,0.1697787,0.0004638271,0.00002778413,0.8257526,0.0008910271],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01136547,0.00015026,0.006992909,0.0001592544,0.001092393,0.001016518,0.9789541,0.0000542728,0.0002148596],"genre_scores_gemma":[0.0004322634,0.0006158353,0.03246799,0.00002427635,0.0004811956,0.000001405631,0.9639319,0.0001657132,0.001879422],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1690495,"threshold_uncertainty_score":0.9997443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161791187706116,"score_gpt":0.3373404802837767,"score_spread":0.2211613615131651,"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."}}