Chemoreception in invasive rusty crayfish (Orconectes rusticus): learning and adaptation in aquatic ecosystems of Northwestern Ontario
Bibliographic record
Abstract
Crayfish utilize chemosensory cues, in addition to other sensory inputs, to mediate a \nvariety of fundamental life processes. Exotic species, like the rusty crayfish (Orconectes \nrusticus), are known to employ a broader range of chemosensory stimuli owing to their \nsuperior adaptability and behavioural plasticity relative to native crayfish species. The \nability to respond rapidly to changing biotic and abiotic conditions contributes to the \nsuccessful establishment of many introduced species in newly adopted ecosystems. I \nreport two behavioural studies designed to measure chemically mediated associative \nlearning, and environment-specific chemical cue utilization, in rusty crayfish. I found that \nrusty crayfish could quickly and easily form a learned attraction to a walleye (Sander \nvitreus) egg cue when paired with a food stimulus using a single, two-hour exposure. I \nalso found that rusty crayfish from two ecologically distinct habitats responded \ndifferently to sympatric v. allopatric conspecifc injury cues. Specifically, both \npopulations tested were attracted to injury cues from a lake where crayfish were likely to \ncannibalize with higher frequency, but showed no response to the same cue from the \nother study lake. My results help describe how aquatic invasive species use chemical \ninformation in their environment to facilitate adaptive responses and survival in new and \nunfamiliar ecosystems. Observations are discussed in the context of relevant literature \nand theory.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".