What is the Relationship Between Waterfowl Population and the MPN of a Zooâs Pond in Indiana
Bibliographic record
Abstract
MCMICHAEL, Kaitlin N., Geosciences, Indiana University-Purdue University Ft. Wayne (IPFW), Fort Wayne, IN 46805 and ISIORHO, K. Solomon, Geosciences, Indiana University - Purdue University Fort Wayne (IPFW), Fort Wayne, IN 46805-1499, mcmikn1110@gmail.com As part of my upper level environmental class, I chose to examine the impact of the increasing Canadian geese population on water quality. Within the last 40 years, Indiana has seen a substantial growth in its population of Canadian Geese. This rise in population may mean that a larger amount of fecal matter is being produced, and may be detrimental to the water quality and other pond inhabitants. To test this hypothesis, water samples were taken from a pond in a city zoo, in the Midwest where a substantial growth in goose population has been observed, and compared to a retention pond of roughly the same shape and size with a much lower waterfowl population. The water samples, for the zoo pond and the residential pond were tested using the most probable number test (MPN), and both samples were then tested for E. coli using eosin methylene blue (EMB) agar. The water from the pond in the zoo had a very high bacterial count (240/100ml) and tested positive for E. coli. The water from the residential pond with a substantially smaller waterfowl population had a very low bacteria count (43/100ml) and the E. coli test was negative. Although it is an ongoing project, the current results seem to point to the fact that larger populations of waterfowl may have a negative effect on the water quality of the ponds they frequent.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".