Going over the edge: Why are there more thrips per flower when there are more flowers in a patch?
Bibliographic record
Abstract
Poster I presented at Canadian Society for Ecology and Evolution meeting in 2011. Still haven't published the study, which I conducted as an undergraduate during a field course in Southern Ontario, Canada. I will post the raw data soon. I would be happy to answer any questions about it. Abstract Many organisms occur in habitat which is naturally fragmentary or patchy, and possibly increasingly so, due to human induced changes. It is therefore important to understand the impact of patch properties such as size on population dynamics of individual organisms. Thrips (Thysanoptera) are a common and widespread generalist herbivore which most often feed in flowers, a naturally patchy resource. I looked at whether the density of a common thrip species in Oxeye Daisy (Asteraceae: Chrysanthemum leucanthemum) flowerheads was related to the size of a daisy patch, measured as the number of flowerheads contained within it. I counted all thrips found in 10 flowerheads at both the centre and edge of 15 patches of varying sizes. Thrips density was positively related to patch size for both central and edge flowers, but the relationship was significantly weaker for the edge. To see whether this relationship could be explained by random movement alone, I developed a simple movement simulation, and fit its results to the data from daisy patches. The model fit well and reproduced many features of the data. It did however, consistently underpredict the densities at patch edges, suggesting that somewhat counterintuitively, densities at the edge may show the strongest signal of any deterministic factors which may increase density as a function of patch size. Overall, the model suggests that a positive relationships between patch size and density should perhaps be considered the null expectation, as opposed to a lack of a relationship, as is more often assumed.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".