Soil phytoliths from miombo woodlands in Mozambique
Bibliographic record
Abstract
Abstract This paper describes topsoil phytolith assemblages from 25 loci underneath miombo woodlands on an eco-transect intersecting the Mozambican Rift along a geographical, altitudinal, climatic and botanical gradient. We provide the first comprehensive overview of the phytolith spectrum that defines northern Mozambique's Zambezian floristic zone. Our classifying criteria derive from comparison with previously described and quantified reference collections of trees and grasses growing in the study area. We characterize the sedimentological and soil features of the matrices where phytoliths are found, establishing correlation among geo-edaphic variables and phytoliths. Descriptive statistics along with nonparametric and parametric statistical analyses evaluate phytolith grouping criteria, variation, robustness, and membership. From a taphonomic perspective, we attest that topsoil phytolith assemblages are polygenic and do not represent an episodic snapshot of extant vegetation, but a palimpsest from plants representing various disturbance episodes, succession stages, and ecological trends. Phytoliths retrieved from Mozambican miombo soils do not seem to trace altitudinal, temperature, or precipitation gradients, and no significant differences exist between highland and lowland phytolith assemblages. This article provides a phytolith analog for woodland environments that can guide future paleoenvironmental research. It also confirms that phytolith analysis is able to detect shifts in the woodland/grassland interface.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".