Editorial: Compensatory growth: an adaptation to environmental stress in plants and animals
Bibliographic record
Abstract
Compensatory growth: an adaptation to environmental stress in plants and animalsCompensatory growth (CG) can be defined as increased growth rate of a previously restricted organism, and has been documented in a wide range of organisms in both the plant and animal kingdoms.Notably, CG can be expressed at the individual, population or even community level as illustrated in the research of this Research Topic.This widelyobserved phenomenon has been of interest to scientists for more than a century because it directly impacts our understanding of life-history trade-offs and resource productivity, respectively.Interest in CG continues to be a stable Research Topic of interest with on average 463 paper/year in the last decade (i.e.Web of Science, search term "compensatory growth", time-period 2014-2023, minimum annual records 398, maximum 522; as of 2024-01-10).Although compensatory growth is common, it may manifest itself in different ways from exact compensation (often referred to as catching-up growth) to under or over compensation where the comparison is with a non-restricted (i.e.control) group (Figure 1).As such, the variability in compensatory growth is as interesting as the phenomenon itself as it often has severe life-history consequences, e.g.costs on other individuals´traits (Metcalfe and Monaghan, 2001).And, as is the case for many biological phenomena, crosstaxa consideration has the potential to explain both the inner workings and general principles associated with compensatory growth.In this Research Topic, we have assembled 10 papers on various topics related to compensatory growth.In Kong et al., the authors used controlled experiments to study the impact of dryseason irrigation and fertilizers on growth of Eucalyptus stands via enhanced litterfall decomposition.Their work demonstrates the importance of elucidating the drivers of CG, in this case, release of soil nutrients, a key limiting resource for compensatory growth in trees.An ecosystem perspective was taken by Zhou et al. to examine how grasslands recover from drought as a form of CG.Furthermore, the authors consider both aboveground and belowground processes among plants and soil-based microorganisms as determinants of ecosystem level response, post-drought.Here, it is also important to keep in mind that CG Frontiers in Plant Science frontiersin.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.026 | 0.025 |
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".