A comparison of Holocene testate amoeba assemblages and paleohydrological records from pollen slides and wet-sieved peat
Bibliographic record
Abstract
In this study, we compared new testate amoeba records produced by the conventional water-based wet-sieving method to testate amoeba data from previous palynological analyses of two Holocene peat profiles from British Columbia, Canada. We used 132 paired samples from the same peat cores to compare the composition of testate amoeba assemblages between the two methods and evaluate the potential of palynological testate amoeba records as quantitative paleohydrological proxies in peatlands. Our results suggest that the palynological treatments (acetolysis, 10% KOH and 150 µm sieving) substantially reduce test concentrations, diversity and richness in most pollen-slide samples. The smaller sieve size used in the palynological treatment (i.e. 150 µm) removed only a small fraction of the total tests, but could introduce a species-specific bias in assemblage composition. However, major shifts between wet and dry conditions, as shown by the water table depth (WTD) reconstructions using wet-sieved samples, were relatively well captured by the palynological datasets in most instances. The palynological assemblages with counts of >50 tests ( n = 30) provided WTD estimates similar to those inferred from the wet-sieved samples as the most dominant hydrophilous and xerophilous taxa were relatively well represented in the pollen-slide samples in the two studied peat cores. Testate amoeba data from pollen slides can provide useful paleohydrological information particularly when combined with other paleoenvironmental proxies. Nonetheless, if the goal is to generate quantitative paleohydrological reconstructions, we recommend that testate amoeba analyses be conducted using the wet-sieving method, as testate amoeba concentrations, diversity and richness are typically much lower in samples prepared with palynological treatments.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".