The Impact of Long-Term (4 Months) Exposure to Low pH and Elevated Temperature on the Growth Rate of Gold Mollies’ (Poecilia Sphenops) Larvae
Bibliographic record
Abstract
Researchers in the marine ecosystem have documented the significant impacts that anthropogenic ocean acidification has on marine organisms. These include olfactory abilities in fish, impaired behavioral as well as physiological changes, including anti-predatory response leading to consequences in population dynamics and community structure. In this research, we endeavored to investigate and compare the growth rate of the gold mollies (Poecialia sphenops) larvae under a low pH of 5 water temperature of 28 O C, and a pH of 6.9 at a water temperature of 26 O conditions. The mollies larvae were weighed for four months (August, September, October, and November) and the data collected was analyzed using the Statistical Package for Social Sciences (IBM SPSS). The analysis was a multivariate test for a more complete examination of data by looking at independent variables and their relationship to one another. There was no statistically significant difference in the growth rate in August (p-value 0.969) and September (p-value 0.286) between the larvae in aquarium A (experimental) and those in aquarium D (control) at the beginning of the experiment. But there was a statistically significant difference in the third (3) month (October) P-value = 0.007 and in the fourth month (4) (November) P-value = 0.004. The low pH of 5 impacted the growth rate of the Poecilia sphenops larvae while those in the control aquarium pH of 6.9 seemed to have not been affected and grew well.
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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".