Coupling of boreal forests and lakes: Effects of conifer pollen on littoral communities
Bibliographic record
Abstract
Conifer pollen deposition is a visually striking spring event in boreal lakes, representing a potentially major allochthonous input of limiting nutrients. We conducted a lake survey and mesocosm experiment at the Experimental Lakes Area in northwestern Ontario to test the hypothesis that jack pine (Pinus banksiana) pollen inputs subsidize littoral nutrient levels and stimulate algal growth and zooplankton abundance. A series of floating litterfall collectors were deployed along transects that span a 0.27-km2 headwater lake (Lake 373) and monitored after ice-out to quantify pollen deposition over a 45-d period. Lake 373 (L373) received 11–56 µmol P m−2 d−1 from pollen, or an annual total of about 10 kg of P. These data were used to determine pollen amendment levels (ambient, 3×, 10×) for an experiment involving 18, 1-m3 littoral mesocosms distributed over three lakes (L239, L373, and L442). Pollen amendments significantly increased total phytoplankton and herbivorous zooplankton biomass, resulting in greater abundance of inedible filamentous green algae and large diatoms. Pollen also exerted a positive lake-specific effect. Periphyton biomass also increased in response to pollen additions, especially filamentous green algae and diatoms in L239 and L442. Conifer pollen subsidizes nutrient levels and promotes production in small boreal lakes.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".