Logging residue sampling methodology for Northeastern Ontario
Bibliographic record
Abstract
The objective of this study was to develop and test \nstatistically justifiable methods of estimating logging \nresidue in cutover areas of northeastern Ontario. Two \nsampling designs and ten sample units were chosen and tested \nusing computer simulation in both finite and infinite sample \nframes for six cutovers with merchantable residue. All six \npopulations showed clustered spatial distributions. Degrees \nof clumping were strongly related to residue density rather \nthan cutover type. Precision of estimating residue volume \nwas poorer than that of estimating residue density. \nMeasuring butts only on plots or narrow strips resulted in \npoor estimation of residue density because of void sample \nunits. Measuring partial logs or using transects achieved \nhigher precision of estimation. A circular transect design \nwas developed for avoiding biased estimation caused by \nresidue orientation. The use of circular transects resulted \nin better estimates than double or triangular transects. \nSystematic sampling using randomly oriented transects is \nunbiased but gave no advantage over simple random sampling. \nRandom sampling with poststratification using circular \ntransects and simple random sampling measuring partial logs \non narrow strips are two alternatives to single line transect \nmethods. However, none of the above methods could provide \nprecise estimates of residue pieces per hectare for cutovers \nwith low densities of residue. The reliable minimum estimate \nmethod could apply to residue inspection in certain low \ndensity cutovers, but no satisfactory results for cases with \nvery low density of residue (less than 17 piecesper hectare) \noccurred. Alternate methods of assessing stumpage aimed at \neliminating the problem of residue should be investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".