The influence of fragment type, time, and coral host genotype on early growth of nursery reared <scp><i>Acropora cervicornis</i></scp>
Bibliographic record
Abstract
Identifying factors that influence nursery coral growth can help estimate nursery productivity and optimize yield. We examined the growth rates of Acropora cervicornis fragments from a nearshore nursery in Fort Lauderdale, Florida to determine how time after fragmentation, fragment type, donor colony nursery structure, and genotype influenced growth. Growth rates were calculated from linear extension measurements of fragments representing 10 coral host genotypes 27 and 61 days after fragmenting. Fragments were classified as apical (obtained from branch tips) or proximal (obtained midbranch), based on photographs. Donor colony nursery structure did not influence growth. A linear mixed‐effects model was performed to assess the influence of time, fragment type, and genotype on growth. Growth rates at 61 days were faster than at 27 days, and apical fragments grew faster than proximal fragments in 2 out of 10 genets at 61 days, providing some evidence for a trade‐off between growth and healing. We found no significant genet growth differences at 27 days. However, differences between genets were detected in 61‐day growth rates, suggesting that growth differences among distinct A. cervicornis genets propagated by asexual fragmentation may not emerge before 2 months, regardless of donor colony structure or fragment type. Our results suggest that restoration practitioners need not monitor growth between genets before 2 months to save resources from ongoing monitoring. In addition, scientists using coral fragments in experiments should be aware that growth differences between genets may take longer than 60 days to appear, posing some risk of confounding experimental results.
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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 | 0.000 | 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) | 0.001 | 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".