{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007376858,0.001924985,0.0009883045,0.002025457,0.0008534417,0.00105807,0.00220774,0.001306178,0.01272589],"category_scores_gemma":[0.001507443,0.0003977199,0.0009231124,0.004072249,0.000434967,0.000645135,0.001177015,0.001237508,0.02823135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197992,"about_ca_system_score_gemma":0.00156767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04749796,"about_ca_topic_score_gemma":0.08084576,"domain_scores_codex":[0.9993802,0.00006052691,0.00005925994,0.0001847614,0.0001933823,0.0001219096],"domain_scores_gemma":[0.9992914,0.0001085735,0.00007697682,0.0001619401,0.0002563263,0.0001047844],"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":[0.0002101489,0.0001796806,0.007644953,0.0005350145,0.00007666271,0.00008899131,0.00007036623,0.001448612,0.0008333651,0.0005271291,0.9785387,0.00984642],"study_design_scores_gemma":[0.0006624887,0.00007694937,0.05935039,0.0002424486,0.0000866582,0.0001895414,0.0004508991,0.004289453,0.002294314,0.001127453,0.9311338,0.00009550729],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002792566,0.0000454384,0.0001357438,0.00005763135,0.0000204473,0.00003139449,0.9955508,0.0003360887,0.001029874],"genre_scores_gemma":[0.001306061,0.0000163691,0.0002991415,0.00001861238,0.000003265218,0.00005154321,0.9978422,0.00002038225,0.0004423879],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04749796,"threshold_uncertainty_score":0.09444296,"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."}}