Extreme weather events alter planktonic communities in boreal lakes
Bibliographic record
Abstract
Climate warming has been shown to increase the frequency of extreme weather effects on small lakes by increasing the variability of terrigenic inputs and surface water temperatures. We hypothesized that the effect of thermal variability on boreal plankton depends on dissolved terrigenic matter (i.e., temperature‐terrigenic interaction). A two‐factor mesocosm (1500‐L capacity) experiment consisting of three terrigenic treatment levels (control, [−] runoff, [+] runoff) and three temperature treatment levels (control, warm, and cold) was conducted in triplicate for a total of 27 mesocosms deployed in Lake 302S of the Experimental Lakes Area in Canada. The warming treatment amplified the positive effect of terrigenic amendment on total phytoplankton biomass by stimulating large (>35‐µm Greatest Axial Linear Dimension; GALD) taxa during the 50‐d experiment. In comparison, removal of terrigenic matter increased the abundance of smaller (<35‐µm GALD) phytoplankton along with copepods and cladocerans under cold and warm conditions, respectively. We also attempted to corroborate our experimental findings by comparing planktonic communities collected from reference Lake 239 during climatically contrasting summers between 1970 and 2001. Although planktonic communities in Lake 239 also differed significantly between years characterized by cold, wet vs. warm, dry ice‐free conditions, their responses ran opposite to those detected during the experiment, highlighting the potential overriding importance of other scale‐dependent factors (e.g., fish predation, vertical migration) mediating the effects of climate on lake communities.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".