A comparison of methods for estimating the age of hollow oaks
Bibliographic record
Abstract
We examined 6 direct (i.e., based on tree ring counts) and 2 indirect (i.e., based on correlations between age and tree characteristics) methods to estimate the age of hollow trees. The errors associated with methods were compared by simulating rotten centres of different sizes (diameter: 5–82 cm) in tree ring cores from sound old oaks (Quercus robur) (diameter: 17–129 cm) collected from pastures in southeast Sweden. The lowest error (mean deviation: 15%) was obtained using tree ring data from the sampled hollow trees in combination with a function based on the growth pattern of sound trees. Indirect methods resulted in moderate errors (mean deviation was 23% and 26% for a function with bark crevice depth and with tree diameter, respectively). Because rotten centres often develop asymmetrically, we found it desirable to take a minimum of 2 cores from each hollow tree, from different cardinal directions. Trunks with a high probability of having a rotten centre could be identified before coring, as the width of the rotten centre increased with increasing bark crevice depth, with increasing size of any entrance hole, and with decreasing distance between the lowest entrance hole and the ground.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".