Effects of shoot age on leaf growth in the seagrass Thalassia testudinum in Barbados
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effects of shoot age on leaf growth variables of Thalassia testudinum were investigated in St. Lawrence Bay, Barbados. Effects were investigated separately within 3 groups of shoots: shoots <1 yr (young shoots, YS), shoots between 1.8 and 2.2 yr (young mature shoots, YMS) and shoots between 4.5 and 6.1 yr (old mature shoots, OMS). Shoot age affected all leaf growth variables, but effects were typically strongest in YS. Leaf width, leaf growth rate, plastochron and maximum leaf length all increased with shoot age, which accounted for 46, 21, 46 and 55% of the variance in YS, respectively. Weak positive effects of shoot age were detectable in YMS and OMS for leaf growth rate, and in OMS for plastochron and maximum leaf length. Number of leaves per shoot increased with shoot age in both mature categories, but the variance explained was low. Shoot age did not affect leaves per shoot in YS. Relative leaf growth rate was negatively correlated with age in YS, not correlated with age in YMS and positively, but weakly, correlated with age in OMS. The possibility of trend reversals between leaf growth variables and shoot age emphasises the need to assess effects of age within discrete age categories. For all leaf growth variables except leaves per shoot, the amount of variance in growth explained by shoot age was substantially higher in YS than in both mature age categories. Results from 2 previous studies and the results of the present study suggest that shoot age effects on leaf growth variables may be common in seagrasses, and that shoot age may have been largely overlooked as a component of the spatial and temporal variation in leaf growth typically observed in seagrasses.
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.
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 | 0.000 | 0.000 |
| 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 it