{"id":"W6906379617","doi":"10.17605/osf.io/vgc3z","title":"Data and code for: Measuring ecological grief to guide inclusive forest management","year":2025,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Code (set theory); Grief; Forest management","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":[0.0222032,0.001052941,0.0008149426,0.00543831,0.002259922,0.004863945,0.002144587,0.002804412,0.06414711],"category_scores_gemma":[0.13414,0.001284709,0.0005960549,0.005895854,0.002331491,0.005891683,0.00579918,0.003200927,0.05073299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006035962,"about_ca_system_score_gemma":0.02572452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1084703,"about_ca_topic_score_gemma":0.0913309,"domain_scores_codex":[0.9799376,0.004895869,0.00428065,0.000970836,0.008648694,0.001266269],"domain_scores_gemma":[0.8432929,0.03883275,0.009137119,0.03134389,0.07079997,0.006593357],"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":[0.0001391621,0.0001012128,0.008425419,0.0003063435,0.00001095048,0.00005041804,0.0007069238,0.0008778692,0.0007297245,0.02173977,0.8997783,0.06713384],"study_design_scores_gemma":[0.0001168045,0.00002922575,0.02705365,0.0007575989,0.00001297685,0.0001034659,0.0007536201,0.002866169,0.002969265,0.02316839,0.9419633,0.0002056173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01288499,0.0004521206,0.1265879,0.02121338,0.00300302,0.009282075,0.6095026,0.02471246,0.1923616],"genre_scores_gemma":[0.07200348,0.0007925479,0.2908541,0.008194431,0.0005655457,0.02247818,0.4853728,0.01350911,0.1062298],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1084703,"threshold_uncertainty_score":0.2156778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02434816793610712,"score_gpt":0.2925380302010228,"score_spread":0.2681898622649156,"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."}}