Adapting a propane turkey fryer to manipulate temperature in aquatic environments
Bibliographic record
Abstract
Abstract There is a growing need to better understand the potential impacts of altered thermal regimes on biodiversity and ecosystem function as mean temperatures, and the likelihood of extreme temperatures, continue to increase. One valuable approach to identify mechanisms and pathways of thermally driven change at the community level is through the manipulation of temperature in the field. However, where methods exist, they are often costly or unable to produce ecologically relevant changes in temperature. Here, we present a low‐cost, easily assembled and readily customizable thermal manipulation system for tide pools or other small bodies of water—the Seaside Array for Understanding Thermal Effects (SAUTE)—and demonstrate its ability to effectively alter the temperature in tide pools. During our 3‐hr heating manipulation, heated pools reached temperatures 4℃ warmer than unmanipulated pools. During the cooling manipulation, cooled pools remained on average 1.8℃ cooler than control pools. The novel SAUTE system can be used to alter the temperature of tide pools in situ. Furthermore, it could be modified to heat other environments such as freshwater vernal pools and settlement tiles in a realistic and meaningful manner, serving as a useful tool to test questions surrounding the relationship between climate warming, thermal variability and ecological processes in natural aquatic communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".