Effect of Crowding and Plant Height on Insect Abundance
Bibliographic record
Abstract
This experiment was done at the grasslands at york university, Toronto Canada over the course of two days between the hours of 2:30 PM and 5:30 PM. On day one the weather was approximately 13 Degrees C and mostly sunny and day 2 was partly cloudy and approximately 10 Degrees C. In this experiment 40 plastic bowls (each day totaling 80 bowls), all of the same colour were placed randomly within the grasslands and filled at least one quarter the way full with soapy water. After all bowls were placed a 1m by 1m quadrat was placed around each bowl and the crowding of living or dead plant matter within the quadrat was estimated from 0-4 (0 meaning no plant biomass 4 meaning no ground could be seen through the plants). Afterwards, the average plant height was estimated with the use of a transect. The tallest and shortest plants were measured, the estimation was made based upon how much area the talest and shortest plants took up within the quadrat and the measurement was recorded in inches. after all measurements had been taken, the bowls were left for 2 hours. When the 2 hours were up the bowls were collected and the number of insects caught in the bowls were counted and recorded. This experiment was conducted to see how the abundance of insects were effected by plant height and plant crowding. This data can be useful to see which, crowding or height, effects insect abundance more. It is also interesting to note that even though the temperatures for both days of the experiment were close, only 3 degree difference, there were significantly fewer insects on the second day of testing.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".