Species‐environment relationships of coastal diatoms from the Sept‐Îles region, Gulf of St‐Lawrence (Québec, Canada)
Bibliographic record
Abstract
Abstract As climate change is having increasingly visible impacts on coastal regions, it is urgent to better understand its effects on the state of ecosystems and the services they provide. To assess the direction and magnitude of change in the high‐use waters of the Sept‐Îles region in the Gulf of St‐Lawrence, we sampled 35 sites along the coast and explored the relationship between surface diatom assemblages and 21 environmental variables. Temperature (14.62%), Secchi depth (12.97%), dissolved silica (6.91%), and magnesium (6.23%) were determined to be significant and independent variables explaining variation in diatom distribution, together explaining 28.6% of the assemblage variance. Temperature and Secchi depth met the λ 1 /λ 2 > 0.5 criterion, but only temperature was selected to develop a transfer function, as these two variables were strongly correlated. A diatom‐based temperature transfer function was then developed using weighted‐averaging partial least squares (WAPLS; 3; r 2 = 0.59, RMSEP = 0.45°C), based on a training set of 32 sites and 55 benthic taxa. However, the model exhibited sensitivity to spatial autocorrelation and may have been influenced by secondary and unmeasured variables. Despite these limitations, the model provides valuable insight into the spatial variability of diatom assemblages and offers potential for paleoenvironmental reconstructions. For optimal inferences, the model should be applied within the studied area. This study contributes to understanding how coastal diatom assemblages respond to environmental gradients and highlights the importance of diatom‐based monitoring to assess ongoing ecological changes in aquatic environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".