How Does a History of Exposure to Varying Concentrations of Salt Influence the Evolution of Salt Tolerance in Ceriodaphnia When Subject to an Acute LC50 Test?
Bibliographic record
Abstract
Introduction An increase of salinity in freshwater ecosystems can cause a loss of biodiversity and affect ecological function (Cañedo-Argüelles et al., 2016). Zooplankton are a key bioindicator when looking at the problem of salinization of freshwater environments. Evolutionary rescue is the “recovery and persistence of a population through natural selection acting on heritable variation” (Bell, 2017). This is relevant to my research because I am exploring evolutionary rescue within Ceriodaphnia populations; Specifically, I am researching salt tolerance within Ceriodaphnia. Thus, my research question is, “How does a history of exposure to varying concentrations of salt influence the evolution of salt tolerance in Ceriodaphnia when subject to an acute LC50 test?”. Methods The Ceriodaphnia used for this project were acquired from a previous study done at the Prairie Wetland Research facility. The Ceriodaphnia were cultured in mesocosms of varying salt concentrations ranging from 22mg/L of Cl to 2000mg/L of Cl including two control mesocosms. To begin, I first collected an individual Ceriodaphnia from a respective mesocosm concentration and added it to a test tube with COMBO media. I then repeated this so that I had 10 test tubes with a singular Ceriodaphnia from a single tank; This was done for each tank. To determine the evolved tolerance of Ceriodaphnia, I will be using acute 48 hours LC50 tests. The Ceriodaphnia will be tested at seven different salt concentration levels. To know if there is an evolved tolerance I will use the R package ‘drc’. Implications This research is important as it can inform policy and decision making practices surrounding environmental health within governmental bodies. Additionally, by understanding the evolved tolerance of freshwater organisms, we can use the information to begin remediation efforts. I expect that the outcomes of this research will exemplify that Ceriodaphnia are able to evolve a tolerance to salt with a history of exposure.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".