Assessing the Interpretability–Performance Trade-Off of Artificial Neural Networks Using Sentinel Fish Health Data
Bibliographic record
Abstract
A number of sentinel species are regularly sampled from the environment near the Oil Sands Region (OSR) in Alberta, Canada. In particular, trout-perch are sampled as a proxy for the health of the aquatic ecosystem. As the development of the OSR began before the environmental monitoring program was in place, there is currently no established measure for the baseline health of the local ecosystem. A common solution is to calculate normal ranges for fish endpoints. Observations found to be outside the normal range are then flagged, alerting researchers to the potential presence of stressors in the local environment. The quality of the normal ranges is dependent on the accuracy of the estimates used to calculate them. This paper explores the use of neural networks and regularized regression for improving the prediction accuracy of fish endpoints. We also consider the trade-off between the prediction accuracy and interpretability of each model. We find that neural networks can provide increased prediction accuracy, but this improvement in accuracy may not be worth the loss in interpretability in some ecological studies. The elastic net offers both good prediction accuracy and interpretability, making it a safe choice for many ecological applications. A hybridized method combining both the neural network and elastic net offers high prediction accuracy as well as some interpretability, and therefore it is the recommended method for this application.
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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 | 0.028 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".