{"id":"W2999660945","doi":"10.5558/tfc2019-024","title":"The Petawawa Research Forest: Establishment of a remote sensing supersite","year":2019,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Canadian Forest Service","funders":"","keywords":"Remote sensing; Benchmarking; Pace; Download; Environmental science; Canopy; Lidar; Computer science; Geography; World Wide Web; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.005027789,0.0006223696,0.0007694909,0.003870084,0.00578477,0.003289587,0.003933903,0.001027904,0.01047729],"category_scores_gemma":[0.004808963,0.000823605,0.0005581601,0.007215681,0.0014042,0.002484087,0.002903989,0.002120167,0.005009878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02114319,"about_ca_system_score_gemma":0.05619794,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9449026,"about_ca_topic_score_gemma":0.9792562,"domain_scores_codex":[0.9950458,0.0003145921,0.0001921076,0.0008736995,0.002735391,0.0008384516],"domain_scores_gemma":[0.9868728,0.0005906366,0.0004738127,0.001937916,0.00767334,0.002451596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001001531,0.0008464315,0.1504596,0.0008727078,0.000174851,0.001020486,0.005330616,0.003640706,0.01854762,0.01018743,0.5706065,0.2373115],"study_design_scores_gemma":[0.0001852187,0.0001279933,0.2355155,0.0003174496,0.00004255154,0.0001990692,0.003562592,0.00788895,0.003839028,0.0007905624,0.7473858,0.0001453801],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2289711,0.001757232,0.04227496,0.007002374,0.0008770754,0.01447412,0.555323,0.006840099,0.1424799],"genre_scores_gemma":[0.2142568,0.001006396,0.1981279,0.001382504,0.000252351,0.006536104,0.5191672,0.002001766,0.05726895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0550974,"threshold_uncertainty_score":0.1534053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02007826028652271,"score_gpt":0.2762048557009328,"score_spread":0.2561265954144101,"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."}}