Evaluating diatom‐derived Holocene pH reconstructions for Arctic lakes using an expanded 171‐lake training set
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Inference models from diatoms preserved in lake sediments can be used to reconstruct long‐term pH changes to better understand the process of lake ontogeny. An expanded diatom training set was developed using taxonomically harmonized modern assemblages in surface sediments of 171 lakes spanning a variety of geological and climatic settings across the Canadian Arctic. Lake‐water pH emerged as a significant variable and the most influential in structuring diatom assemblages. The resulting two‐component weighted‐averaging partial least squares pH inference model performs strongly, even after identifying effects of spatial autocorrelation at distances <20 km. The model was then applied to three dated Holocene diatom stratigraphies from Arctic regions of contrasting bedrock geology and buffering capacity, and the significance of the pH reconstructions was assessed. At Lake CF3 in a poorly buffered catchment, a gradual but significant pH decline begins 5000 years after lake inception, coincident with regional Late Holocene cooling. Reconstructions for two well‐buffered, more alkaline sites were not significant, probably due to poor analogues and other factors driving changes in diatom assemblages. Due to sparse soil and vegetation in these and other Arctic basins, bedrock composition is the most important regulator of Holocene pH, and only in poorly buffered lakes does pH primarily represent a climate signal.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it