Predicting the age of ancient <i>Thuja occidentalis</i> on cliffs
Bibliographic record
Abstract
In rocky, heterogeneous environments that support old-growth forests, the relationship between tree size and age is weaker than it is for trees growing in productive and homogeneous habitats. To assist in the management and conservation of ancient forests on rocky land of low productivity, it would be useful if the relationships among age, environmental heterogeneity, and morphological variability could be understood and used to develop predictive models of longevity so that extensive core sampling of trees would not be required. Here we sampled 296 mature Thuja occidentalis L. growing on limestone cliffs along the Niagara Escarpment, southern Ontario, Canada. We measured a variety of site conditions and morphological traits, including age, which varied from 51 to 1316 years. We then used redundancy analysis and multiple regression to model the relationships among age, morphology, growth rate, and environment, resulting in quantitative models predicting tree age from four subsets of variables. We subsequently tested the models on 60 additional trees not used to build the models and found that they predicted up to 78% of the variation in actual tree age. This approach could be adopted for use in other forest types to predict the age of trees without using tree-ring analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".