Is long primary growth associated with stem sinuosity in Douglas-fir?
Bibliographic record
Abstract
Stem sinuosity is a highly visible stem-form trait in the leaders of fast-growing Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) trees, yet its cause is unknown. We tested the hypotheses that sinuous stems have longer expanses of primary growth than nonsinuous stems (putting the leader at higher risk for curvature, induction of compression wood formation, and possibly overcorrection) and higher leader angle using 4- to 5-year-old saplings in raised beds. As hypothesized, sinuous stems had longer expanses of primary growth than did nonsinuous stems (13.5 vs. 12.3 cm, respectively). However, for the dates for which growth (length/day, primary growth, secondary growth, and total growth) differed significantly among sinuosity class, sinuosity class only explained 15%–21% of the variation in growth rate. There were no significant differences in leader angle for saplings of the three sinuosity classes. Contingency tables indicated some consistency in the category of sinuosity to which we assigned the stems in 2001 and 2002 (χ2 = 11.2, p < 0.004). When we used a more quantitative measure, the ratio of stem length/stem distance, there was a tendency toward a significant relationship between the two years (r = 0.272, p = 0.0893). These data suggest that, counter to expectation, the rate of stem growth was not a large factor in determining whether leaders become sinuous for this population of trees.
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 | 0.001 | 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 | 0.001 |
| 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".