Changes in phytoplankton communities following logging in the drainage basins of three boreal forest lakes in northwestern Ontario (Canada), 19912000
Bibliographic record
Abstract
The phytoplankton communities of three small boreal forest lakes (L26, L39, and L42) on Ontario's Precambrian Shield (Canada) were investigated over 10 years for possible effects of forest harvesting (logging) within their drainage basins (5 years before logging vs. 5 years after logging). During the postlogging period, higher biovolumes of several taxa were recorded, consistent with previously reported changes in nutrients, chlorophyll, light penetration, and mixing depth. Among the most dramatic changes were increases of 100 and 266% in Cyanophyceae in L39 and L42, respectively, 167% in Dinophyceae in L26, 51 and 130% in Chlorophyceae in L26 and L42, respectively, 182% in Bacillariophyceae in L26, and 53 and 73% in total phytoplankton in L26 and L42, respectively. Other effects associated with logging in the watersheds of these lakes included an increase in the numbers of taxa (in accordance with the intermediate disturbance hypothesis) and a decrease in interannual variability in phytoplankton community structure (in accordance with the ecosystem diversitystability hypothesis). The less extensive logging of the L26 drainage basin and the maintenance of an unlogged shoreline buffer strip did not preclude apparent effects on phytoplankton comparable with some of those found in the other two lakes, where drainage basin logging was more extensive.
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 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 | 0.002 | 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) | 0.001 | 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".