Effects of spatial variation in benthic phototrophs along a depth gradient on assessments of whole‐lake processes
Bibliographic record
Abstract
Abstract Phytobenthos are often underrepresented in both limnological and paleolimnological studies but may play key roles in whole‐lake production and ecosystem processes including eutrophication, food‐web dynamics, and ecosystem state changes. Photosynthetic pigments, stables isotopes, and diatoms were quantified from surface sediments (0–1 cm) collected across a depth transect of a small, DOC‐rich, mesotrophic lake in boreal northwestern Ontario to assess spatial variation in phytobenthos abundance and production. Maximal concentrations of siliceous algae and cyanobacteria pigments occurred at ~2–6 m depth, with abundant tychoplanktonic diatoms, depleted sedimentary δ13C values, and elevated ratios of precursor chlorophyll a to product pheophytin a, all aligning well with the depths of the thermocline, epilimnetic mixing, and maximum light penetration. These patterns demonstrated the presence of three discrete community assemblages, with greatest mass accumulation occurring at intermediate depths where warm illuminated sediments provide habitat for tychoplanktonic diatoms and cyanobacteria between turbulent shallows and cold and dark depths. If widespread among boreal lakes, this tychoplanktonic zone may exert important effects on whole‐lake production, carbon sequestration, benthic‐pelagic food‐web coupling, eutrophication, and ecosystem state change.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".