{"id":"W4412989850","doi":"10.3390/f16081280","title":"Forest Volume Estimation in Secondary Forests of the Southern Daxing’anling Mountains Using Multi-Source Remote Sensing and Machine Learning","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"Inner Mongolia Agricultural University; National Natural Science Foundation of China","keywords":"Volume (thermodynamics); Remote sensing; Estimation; Forestry; Environmental science; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005424297,0.0005291238,0.0003158895,0.001702535,0.0002862773,0.000512277,0.0004128658,0.0003202415,0.000241635],"category_scores_gemma":[0.0005721565,0.0001821379,0.0004947183,0.0008400373,0.0001859052,0.0007515756,0.0003788302,0.0002063113,0.00007665829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003282762,"about_ca_system_score_gemma":0.0003644743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01551769,"about_ca_topic_score_gemma":0.02836661,"domain_scores_codex":[0.9997997,0.00003205629,0.00001594484,0.00006320624,0.00005520511,0.00003388456],"domain_scores_gemma":[0.9997607,0.00006970832,0.00004955729,0.00002159253,0.00007801774,0.00002032993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001738129,0.0001997933,0.6493509,0.0002325674,0.0002406321,0.0005373082,0.000850238,0.1379264,0.02336384,0.0004901442,0.0007198538,0.1859144],"study_design_scores_gemma":[0.00001338205,0.0000560488,0.4758491,0.0000343195,0.00008099634,0.0001414849,0.0004975051,0.5173583,0.00460811,0.0003437199,0.0009759371,0.00004119181],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905435,0.0002539949,0.008259493,0.00003401251,0.000009415479,0.00001822207,0.0002333099,0.0001379667,0.0005099595],"genre_scores_gemma":[0.9928525,0.0000726599,0.006357998,0.000009149702,0.000006743962,0.00001287458,0.0005081435,0.000008401846,0.0001715454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01551769,"threshold_uncertainty_score":0.0308547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01244609857130051,"score_gpt":0.246847084572977,"score_spread":0.2344009860016765,"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."}}