Algae-based Biomonitoring: Predicting Diatom Reference Communities in Unpolluted Streams using Classification Trees, Random Forests, and Artificial Neural Networks
Bibliographic record
Abstract
Abstract The Eastern Canadian Diatom Index (IDEC) was developed to evaluate the ecological integrity of streams along a pollution gradient, as a function of the dissimilarity between current diatom communities and suitable reference communities. Distinguishing natural variations in community structure from those induced by human activities is essential for proper assessment of dissimilarity. To account for the effect of the natural variation in pH on this assessment, two IDEC subindices were used: one for sites with diatom reference communities typical of naturally alkaline water pH, and another for sites with communities typical of naturally circumneutral water pH. This study used three statistical models, namely classification trees (CT), random forests (RF), and artificial neural networks (ANN) to: (i) identify the environmental variables discriminating between alkaline and neutral reference communities (“biotypes”), and (ii) compare their predictive capacities. Models identified clay rocks, gneiss/paragneiss rocks, siliceous rocks, and carbonated rocks as the main geological features discriminating reference biotypes. For the reference streams, clay, siliceous, and carbonated rocks were associated with high water pH while gneiss/paragneiss rocks were associated with low water pH. Both ANN and RF models behaved similarly across all performance criteria and yielded general models useful for identifying the appropriate IDEC sub-index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".