Extracting environmentally driven growth trends from diameter increment series based on a multiplicative decomposition model
Bibliographic record
Abstract
The study developed a conceptual framework for partitioning the components of diameter increment to potentially detect the influence of environmental changes. This process consisted of two steps. First, a multiplicative decomposition diameter increment model was introduced to evaluate the influence of ageing, site quality, competition status, and thinning effects on individual tree growth. Second, generalized additive models were applied to identify the nonlinear dynamic of growth trends caused by environmental changes. The conceptual framework was then applied to Norway spruce ( Picea abies (L.) Karst.) growing in southwest Germany. The database consisted primarily of tree ring series collected from trees cut from long-term experimental stands. Also, stand-level data were available from periodical remeasurements of these plots. The developed analytical technique effectively removed non-environment-related effects (ageing, site quality, and stand dynamic) from the growth signal provided in the diameter increment series. Growth trends deducted from estimates based on either nonlinear least squares, generalized nonlinear least squares, or nonlinear mixed-effects approaches displayed quite similar patterns. In general, the trend in diameter increment showed a long-term increase from the 1920s into the 1990s with a midterm depression in the 1940s that was followed by a significant decrease in the recent past.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".