The role of hypoxia in a fresh water environment, the ecological implications in a piscine predator-prey relationship
Bibliographic record
Abstract
This study focused on the influence of body size of teleosts on tolerance and the implications in a predator and prey relationship. Body size limitations are evident in many predator and prey relationships and as a result there is the potential for variation in tolerance to hypoxia. It was predicted that prey would have a greater tolerance of hypoxia than its piscine predator. I suggested the difference in body size would account for this difference as some physiological evidence was found to supports this. Three physiological parameters, expected to increase in response to a reduction on dissolved oxygen, were measured in response to hypoxia and were used to determine tolerance. All of the physiological variables measured suggested a size sensitive relationship in which the smaller prey (fathead minnow, Pimephales promelas') was better able to withstand hypoxic conditions than the predatory yellow perch ('Perca flavescens'). Based on this size-sensitive relationship of tolerance to hypoxia, I developed a theoretical model based on the ideal free distribution to determine the distribution of a predator and prey population in response to fluctuating dissolved oxygen levels. (Abstract shortened by UMI.)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".