Comparison of techniques for correlating survival and gene expression data from wild salmon
Bibliographic record
Abstract
Abstract – In laboratory and field studies of survival, one of two alternative analytical techniques is often used to estimate survival rates and identify covariates, namely parametric survival analysis or Cormack–Jolly–Seber models. These techniques differ in algorithms and assumptions of the data. They also tend to be used under different circumstances depending on whether the intention is to demonstrate group‐specific differences or to predict survival variables. Here, we apply and compare both analytical techniques in a study that couples functional genomics with biotelemetry to ascertain the role of physiological condition on survival of adult sockeye salmon ( Oncorhynchus nerka ) migrating in the Fraser River, British Columbia, which builds on the growing concern over the decline in numbers of spawning fish. Herein, we show a high level of quantitative and qualitative agreement between the two analytical methods, with both showing a strong relationship exists between the genomic signature that accounts for the largest source of variance in gene expression among individuals and survival in one of the three populations assessed. This high level of agreement suggests the data and the approaches are generating reliable results. The novel approach used in our study to identify physiological processes associated with reduced fitness in wild populations should be of broad interest to conservation biologists and resource managers as it may help reduce the uncertainty associated with predicting population sizes.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".