Intra‐tree variability in wood anatomy and its implications for fossil wood systematics and palaeoclimatic studies
Bibliographic record
Abstract
Abstract: The validity of using quantitative analyses of wood anatomical characters as systematic tools and as palaeoenvironmental proxies has been questioned on the basis that natural variability, and in particular intra‐tree variability, tends to drown out the signal being sought. A detailed quantitative description of the wood anatomy of a balsam fir tree was undertaken along root–stump–trunk–branch transects to ascertain intra‐tree variability, and to assess noise‐to‐signal ratio. Results demonstrate significant ontogenetic trends for anatomical parameters such as tracheid pit distribution, cross‐field pit frequency, ray dimensions, ray spacing, tracheid diameter, mean ring width and mean sensitivity. However, although intra‐tree variability is great, results suggest that fossil taxa nevertheless may be distinguished from one another on the basis of standard qualitative and quantitative procedures. With regard to palaeoenvironmental studies, results indicate that a significant, but not unrealistic, increase in sample size and an improved knowledge of specimen ontogeny is needed in the future if signals are to be distinguished from background intra‐tree variability for some parameters.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".