Reconstructing the salinity and environment of the Limfjord and Vejlerne Nature Reserve, Denmark, using a diatom model for brackish lakes and fjords
Bibliographic record
Abstract
Diatoms in surface sediments from a data set of 27 brackish lakes and nine fjords in Jutland, Denmark (range 0.2 31 g·L1 total dissolved solids (TDS)), were analysed using multivariate methods to determine response to measured parameters (depth, total phosphorus (TP), total nitrogen (TN), TN/TP, salinity, water body type). Water body type, salinity, depth and TP together explained 25.3% of the variation in the diatom data and were all independently significant predictors. A diatomsalinity model (r2jack = 0.887, root mean square error of prediction = 0.246 log salinity, g·L1) was developed from the 36 sample training set and applied to fossil diatom assemblages in three sediment cores from the east Vejlerne wetland, Denmark, a nature reserve created after the damming of an embayment of the polyhaline Limfjord (~26 g·L1 TDS) in the late 19th century. The diatominferred salinity reconstructions reflect the known salinity history of the Limfjord and the freshwatersubsaline Vejlerne lakes, and appear sensitive to documented North Sea storms in the 16th and 17th centuries, which had major impacts on the brackish Limfjord herring fishery. Diatomsalinity models may be useful tools in long-term studies of coastal and estuarine areas to test hypotheses concerning aquatic resources and ecological, hydrographic, and cultural 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".