Inbreeding Affects on Beetle Clustering
Bibliographic record
Abstract
The Red Flour Beetle (Tribolium castaneum; Coleoptera: Tenebrionidae) is a common pest in many grain mills (Baldwin and Fasulo 2010) and found wherever grains or other dried foods are stored (Schröder 2008). The Red Flour Beetle ”facilitates genetic analysis with ease of culture, a short life cycle, high fecundity and facility for genetic crosses, allowing efficient genetic screens (Schröder 2008).” This can allow for them to have a strong genetic code the longer that they are bred in the lab. So, for this experiment we will be trying to see what affect inbreeding has on the aggregation behavior in the Red Flour Beetle. The purpose of this experiment is to see how genetic background influences grouping behavior. Two different strains of Tribolium castaneum were used in this experiment. The one strain used was the Hudson Red Flour Beetle which originates from Hudson, Kansas. They have been bred in the lab for 10 years. The other beetle that was used is the NDG Red Flour Beetle, which originates from Manitoba, Canada and has been in the lab 30 years. After completing this experiment, our findings are that the Hudson are considerably more light sensitive than the Hudson and as soon as light hit them they become very active. There was a noticeably higher percentage of NDG beetles that would cluster together and when they clustered they only clustered with their own strain. This leads us to believe that the NDG beetles have stronger aggregation behavior due to the significant amount of time they have been in lab breeding, compared to the Hudson beetle. The inbreeding that occurred might of allowed for the beetles to develop a more similar genetic code that allows them to group together more easily.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".