Enough is Enough, or More is More? Testing the Influence of Foraminiferal Count Size on Reconstructions of Paleo-Marsh Elevation
Bibliographic record
Abstract
ABSTRACT Salt-marsh foraminifera are sea-level proxies used to quantitatively reconstruct Holocene paleo-marsh elevations (PME) and subsequently relative sea level (RSL). The reliability of these reconstructions is partly dependent upon counting enough foraminifera to accurately characterize assemblages, while counting fewer tests allows more samples to be processed. We test the influence of count size on PME reconstructions by repeatedly subsampling foraminiferal assemblages preserved in a core of salt-marsh peat (from Newfoundland, Canada) with unusually large counts (up to 1595). Application of a single, weighted-averaging transfer function developed from a regional-scale modern training set to these ecologically-plausible simulated assemblages generated PME reconstructions at count sizes of 10–700. Reconstructed PMEs stabilize at counts sizes greater than ∼50 and counts exceeding ∼250 tests show little return for the additional time invested. The absence of some rare taxa in low counts is unlikely to markedly influence results from weighted-averaging transfer functions. Subsampling of modern foraminifera indicates that cross-validated transfer function performance shows only modest improvement when more than ∼40 foraminifera are counted. Studies seeking to understand multi-meter and millennial scale RSL trends should count more than ∼50 tests. The precision sought by studies aiming to resolve decimeter- and decadal-scale RSL variability is best achieved with counts greater than ∼75. In most studies seeking to reconstruct PME, effort is more productively allocated by counting relatively fewer foraminifera in more core samples than in counting large numbers of individuals. Target count sizes of 100–300 in existing studies are likely conservative and robust. Given the low diversity of salt-marsh foraminiferal assemblages, our results are likely applicable throughout and beyond northeastern North America.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".