Correlates and consequences of injury in a large, predatory stream salamander (Dicamptodon tenebrosus)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conspecific aggression is an important factor structuring population dynamics through intra- and interspecific interactions, but is rarely studied in un-manipulated populations. In this study, we evaluated rates of injury as a proxy for conspecific aggression using a depletion survey of predatory coastal giant salamanders ( Dicamptodon tenebrosus ) in a tributary of the South Fork Eel River, California. We tested a range of hypotheses including a suite of environmental and biotic factors for the rate of injury in a population by using an AIC model-selection approach that examined the weight of evidence for individual models. We examined both the probability of a given individual being injured, and the proportion of individuals within a given study pool being injured. We found strong support for models including salamander size, density of young-of-the-year steelhead, and density of the largest size-class of salamander as factors positively influencing the rate of injury at both the individual and habitat levels. We also found that density of older steelhead (1+ steelhead) had a strong, but highly variable positive impact on frequency of injury. This study shows that both conspecific and heterospecific factors influence intraspecific aggression for the dominant salamander throughout coastal Pacific Northwest streams. Our methodology demonstrates a non-manipulative approach to identifying correlates of natural injury in a cryptic species of amphibian. More work is needed to determine how these factors directly and indirectly influence the spatial distribution, individual fitness, and dynamics of salamander populations within streams.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 it