{"id":"W4323845889","doi":"10.3390/f14030548","title":"Forest Resources Projection Tools: Comparison of Available Tools and Their Adaptation to Polish Conditions","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Carbon accounting; Forest management; Forest inventory; Environmental resource management; Stock (firearms); European union; Carbon stock; Scale (ratio); Distribution (mathematics); Sustainable forest management; Business; Land use; Accounting method; Accounting; Forestry; Climate change; Environmental science; Geography; Ecology","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":[0.01043758,0.00135826,0.0007259488,0.006973143,0.0004862355,0.003217674,0.001436274,0.0005963268,0.003269812],"category_scores_gemma":[0.02073872,0.0006434798,0.001403264,0.006544059,0.0004709189,0.005302669,0.00290831,0.0007813806,0.001056677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001402038,"about_ca_system_score_gemma":0.002131198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01807112,"about_ca_topic_score_gemma":0.0125028,"domain_scores_codex":[0.9945354,0.001193504,0.000995894,0.000682565,0.002288581,0.0003040791],"domain_scores_gemma":[0.9929312,0.002586609,0.0005745784,0.001238451,0.002495446,0.0001736677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001597595,0.0003360049,0.1141411,0.002985064,0.001244429,0.0004021678,0.002319273,0.09166528,0.004750265,0.02167195,0.01282799,0.7460588],"study_design_scores_gemma":[0.0003888558,0.0007571792,0.3506393,0.004117145,0.001755862,0.00114954,0.007306958,0.3789552,0.02872574,0.02821648,0.1971319,0.0008557418],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6405818,0.007662005,0.2674813,0.00101266,0.0003392098,0.001101171,0.02654203,0.009568947,0.04571086],"genre_scores_gemma":[0.7284408,0.006524892,0.2383247,0.0001304718,0.00005026008,0.0007784497,0.02125457,0.001914231,0.002581526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01807112,"threshold_uncertainty_score":0.05519986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.069347668293584,"score_gpt":0.2938061594409341,"score_spread":0.2244584911473501,"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."}}