Herring Gull Abundance in parking lots with/without automobile vehicles
Bibliographic record
Abstract
A field experiment was conducted from October 15,2020 to October 17,2020 about how the prevalence of appearance of Herring Gulls in the parking lot is affected by the presence of cars in the parking lot. The three specific locations that were selected are big parking lots that have an open area located within Mississauga Heartland that are about 1km-2 km apart from each other. The optimal time at which the parking lots at the three different locations were empty from cars was between the time of 7 am – 8am and the time the parking lot was full of cars was between 3 pm-4pm. On October 15,2020 I would arrive at location #1 (43.616004N, -79.701848W) at 7 am and observe how many Herring Gulls I can see within my field of view at the parking lot for about 8-10 minutes. Then I would drive to location number two ( 43°34'46.7"N 79°44'02.2"W) and arrive by 7:15 am and I would observe how many Herring Gulls I can spot within my field of view at the parking lot for about 8-10 minutes. I would go to the third location (43.599588, -79.712981) and perform the same exact procedure I did for location one and two. Then later throughout the day, I would arrive at location one at 3pm, location two at 3:15 pm and location three at 3:30 pm and takes notes about how many Herring Gulls I spot at the parking lot within the 8-10 minutes I am there. I repeated the following procedure on October 16,2020 and October 17,2020 to get replicate measurement for data. Before performing the field study, perform a pilot study to make sure that all the parking lots chosen to contain a restaurant chain and a garbage can within the plaza. Also make sure that the 3-day period weather forecast is between 11 – 15 degree with no rain. A car was used to get from location one to location two and three.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".