Bibliographic record
Abstract
INTRODUCTION The fastest growing trees are the eucalypts, one type of which, found in New Guinea, has been known to add nearly 8 m to its height in 1 year. Even these “sprinters” are eclipsed by giant bamboo, which can grow over 1 metre a day, 30 m in under 3 months. At the other extreme, a Sitka spruce found at the tree limit in the Arctic had one of the slowest growth rates on record. From measurements of the annual growth rings in the trunk it was estimated to be about 100 years old yet was only 28 cm tall. The total growth of which some plants are capable in a lifetime is startling. One of the largest giant redwood trees found had a wood volume of more than 1500 m 3 and weighed over 1000 tonnes. Since the seed of the giant redwood weighs less than 0.005 g, the weight increase over the lifetime of this specimen was more than 250 billion times. Large trees like these can live for more than 4000 years, illustrating that plants often combine in their bodies tissues of great antiquity with others that are still youthful, producing new leaves, shoots, roots, fruits, and seeds. CONTROL OF DEVELOPMENT AND GROWTH FORM In animals, organs develop very early in life and become an integral part of the whole organism without which it cannot function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".