Distribution of freshwater diatoms in 64 Labrador (Canada) lakes: speciesenvironment relationships along latitudinal gradients and reconstruction models for water colour and alkalinity
Bibliographic record
Abstract
The distribution of modern diatom assemblages in surficial sediments of 64 Labrador (Canada) lakes across broad vegetational biomes was studied in order to explore speciesenvironment relationships and to develop transfer functions for paleoenvironmental reconstruction. The study sites were situated along a latitudinal gradient (51°27' to 57°37' N) and classified according to six catchment vegetation types: wetland (peatland) forest, spruce/fir forest, lichen woodland, foresttundra, coastal tundra, and tundra. Canonical correspondence analysis revealed that among 28 environmental variables determined for each site, water colour and alkalinity accounted for most of the variance in the diatom data. Using weighted-averaging partial least squares techniques, we developed transfer functions for inference of water colour (CLR) (r2jack= 0.85, root mean square error of prediction (RMSEP) = 0.18log(CLR + 1) or 1.51 Pt units) and alkalinity (ALK) (r2jack= 0.63, RMSEP = 0.25log(ALK + 1) or 1.78 µeq·L1) from the percent abundance of the 132 most abundant diatom taxa. By determining diatom distribution in relation to more detailed vegetation types within the boreal forest zone (wetland forest, spruce/fir forest, and lichen woodland), this calibration data set demonstrated the potential of these assemblages for revealing more subtle changes in lake catchment vegetation over time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".