Using mesocosms as a way to study coastal wetlands
Bibliographic record
Abstract
Key points Anthropogenic impacts have destroyed many salt marshes and mangroves; we are now trying to rebuild them for the ecosystem services they provide; although individual studies and experiments may not answer all questions, they provide valuable insights to effective restoration means; appropriate experiments lead to best methods for achieving high success rates; mesocosms can provide information on effects of future impacts from sea level rise, pollution and biological invasions; more global collaboration with experimental efforts is required to reduce wasted time, energy and finances on overlapping ‘trial-and-error’ experiments and evaluators of success; examples are given of various coastal wetland restoration and construction projects worldwide; there is also strong need for individual research teams to search current subject literature and collaborate with multidisciplinary teams to achieve the best outcomes efficiently and economically. Why make experimental studies in coastal wetlands? There are many ways to study coastal wetlands, for just as many purposes. Experimentally, microcosms, mesocosms, whole-system studies (i.e . in situ ) and even mathematical models (defined in Table 14.1) can give detailed information on the ecology, sedimentology and hydrology of a salt marsh or mangrove system, answering specific research questions that might be missed in basic observational field studies. The purposes of experimental work include pollution impact and remediation, creating and restoring salt marshes, impacts of biological invaders, to modelling effects of sea level rise. This chapter introduces the principles of mesocosm studies and some of the experimental work done in coastal wetlands, giving various global examples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".