Prevalence and ecological features of deep chlorophyll layers in Lake of the Woods, a complex hydrological system with strong trophic, physical, and chemical gradients
Bibliographic record
Abstract
We characterized deep chlorophyll layers (DCLs), previously unknown, in Lake of the Woods (Canada), a complex hydrological system with strong trophic, physical, and chemical gradients. Of 42 sites, five (12 %) contained at least one dense, thin metalimnetic peak – often more, overlapping or vertically stratified. In spring, highest biomass (>4 mg/L) was found at 3 m in Bigstone Bay, composed of a mixed phytoplankton community. Other, deeper, spring DCLs were dominated by the diatom Cyclotella (∼1 mg/L, 21 m), dinoflagellate Gymnodinium (>1 mg/L, 17 m), cyanobacterium Dolichospermum (>3 mg/L, 10 m), or cyanobacterium Woronichinia (>1 mg/L, ∼8 m). In summer, the highest biomass of a DCL was in Yellow Girl Bay (>21 mg/L), over five times higher than spring and higher than large surface blooms from the shallow, eutrophic south (Sabaskong Bay ∼19 mg/L). Summer DCLs (∼7 m) were not as deep as spring, owing to reduced light penetration (2.5-fold lower Zeu:Zmix). Most summer DCLs were dominated by Dolichospermum (max. >16 mg/L) but other species were also high (∼1 mg/L biomass), including cyanobacterium Aphanizomenon, diatom Aulacoseira, and cryptophyte Cryptomonas. Euphotic zone in these embayments intersected with a nutrient-enriched (PO4, Fe, Mn, Si) hypoxic hypolimnion, conditions conducive to DCLs. Other drivers of composition and activity of DCLs remain to be elucidated. These high levels of DCL biomass, up to fivefold higher than near-surface, have routinely gone unreported. Their inclusion in establishing baselines and tracking change would inform lake management decisions and qualify expectations for ecosystem response.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".