Free-to-grow regeneration standards are poorly linked to growth of spruce in boreal mixedwoods
Bibliographic record
Abstract
For public lands in Canada, the free-to-grow (FTG) standards are part of regulatory surveys of post-harvest juvenile stands that are designed to assess the level of competition on conifer trees. In this paper we analyzed two data sets to test the ability of FTG standards to predict growth of white spruce saplings. Using data from juvenile permanent sample plots, FTG was assessed at age 13 and subsequent diameter increment and height increment were assessed in the measurement interval after year 18. If the growth of trees was adjusted for differences in height at age 13, FTG status made no difference in predicting subsequent growth. A second data set from operational regeneration surveys of 49, 13-year-old boreal mixedwood stands was also evaluated for evidence of competition across a range of cutblock with different percentages of plots classed as FTG. There was no evidence of an increase in variation in size or growth of leading trees in stands when the plots in a cutblock were evenly split between FTG and not-FTG trees, compared to cutblocks with either few plots FTG or nearly all plots FTG. Height attained at year 13 was a sufficient indicator of future growth. Key words: regeneration, standard, free, grow, productivity, mixedwoods
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".