Time since death and fall of Norway spruce logs in old-growth and selectively cut boreal forest
Bibliographic record
Abstract
To estimate the age of Norway spruce (Picea abies (L.) Karst.) logs by means of decay classes, and to assess how long it takes for downed logs to decompose, we dated logs dendrochronologically by applying 5- and 8-grade decay classification systems. Study sites were chosen in old-growth and previously selectively cut forest stands in boreal south-central Scandinavia; 113 logs were dated to the number of years since death, 120 were dated to the number of years since fall, and 61 logs were dated to both. The number of years from death to fall showed a negative exponential distribution, with a mean of 22 years and a range of 091 years. Decay classes of logs (8-grade scale) reflected time since fall (R2 = 0.58) better than time since death (R2 = 0.27) in a linear regression model. This result is due to the lower decomposition rate of standing snags. Therefore, the decomposition time of logs should be divided into two periods: time from death to fall, which varies considerably, and time after fall, which appears to follow a linear relationship with decay class. The model predicted that it takes 100 years after fall for downed logs to decompose completely (reaching decay class 8) in old-growth stands. Logs in selectively cut stands appeared to decompose faster (64 years), which is explained by a sample shortage of old logs resulting from previous cuttings. We conclude that the decomposition time of downed logs may be severely underestimated when data is retrospectively compiled from previously logged forest stands.
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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 | 0.001 |
| 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 | 0.000 | 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".