The Effects of Temperature on the Oxygen Production of Chlamydomonas reinhardtii
Bibliographic record
Abstract
As a keystone species in British Columbia, salmon play a large role in the ecosystem as changes in their abundance can greatly affect the numbers of other species nearby (Hilderbrand et al., 2004). Chlamydomonas reinhardtii (C. reinhardtii) are single-celled green algae that are a major food source for salmon (Norambuena et al., 2015). Due to their abundance in streams, their oxygen (O2) production contributes to the overall O2 level of streams and can indirectly affect the health of salmon (Carter, 2005). This experiment tests the effects of simulated seasonal temperatures in Vancouver stream waters on the O2 production in C. reinhardtii. Our goal is to better understand when the most ideal time for salmon to spawn is and the impacts of changing water temperatures due to climate change may have on C. reinhardtii, and consequently on salmon populations. This was conducted by incubating live cultures for 75 minutes in 7oC, 17oC and 27oC waterbaths, and measuring the change in O2 in the different treatments both pre- and post-incubation. Cell counts were performed in order to determine the O2 produced per cell. Our null hypothesis was that temperature has no effect on O2 production. By conducting a One-way ANOVA we obtained a p-value of 0.08.. Since p > 0.05, the differences between the treatments were not significant. The small sample size (n=3) limited the statistical analysis and the power. Had the sample size been larger, the p-value may have been less than 0.05. Overall, our results suggest that temperature does not have a significant effect on the O2 production of C. reinhardtii.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".