Pelagic and benthic algal responses in eastern Canadian Boreal Shield lakes following harvesting and wildfires
Bibliographic record
Abstract
Pelagic and benthic algal biomass and pelagic algal community structure were measured in Boreal Shield lakes impacted by forest harvesting and wildfires (Haute-Mauricie, Québec). Sixteen reference lakes in which the watershed has been unperturbed for at least 40 years, seven harvested lake watersheds (logged in 1995), and nine lake watersheds burnt in 1995 were sampled for 3 years following harvesting or wildfires. From 1996 to 1998, repeated-measures ANOVA showed significant effects between treatment and sampling years for pelagic chlorophyll a (Chl a) and biomass, but for 1997-1998 benthic Chl a, repeated-measures ANOVA showed only significant treatment effects. Chl a concentrations increased 1.4- to 3-fold in perturbed lakes as compared with reference lakes. Areal pelagic Chl a (milligrams per square metre) was lower than estimated littoral Chl a in perturbed lakes. The pelagic algal community was dominated by mixotrophic nanoflagellates in reference lakes. Watershed perturbation induced differential changes in pelagic algal communities: mixotrophic nanoflagellates increased in harvested lakes and photoautotrophic diatoms in burnt lakes. Considering only perturbed lakes, algal biomass was proportional to the fraction of the catchment area perturbed divided by the surface area of lakes in the catchment.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".