Two thousand–year reconstruction of livestock production intensity in France using sediment-archived fecal <i>Bacteroidales</i> and source-specific mitochondrial markers
Bibliographic record
Abstract
The reconstruction of past pastoral activities based on microscopic methods (pollen and coprophilous fungal ascospores) does not accurately identify the domestic species involved. In contrast, source-specific DNA markers, commonly employed in water quality microbial source tracking (MST) studies, may represent a promising tool for retrospectively identifying species-specific fecal contamination in sediment deposition. In the present study, molecular methods were used to quantify Bacteroidales and identify ovine and bovine mitochondrial DNA extracted from sediment cores from two forest hollows comprising 2000 years of deposition. The DNA marker abundance was contrasted with the abundance of ascospores and plant-specific pollen throughout the sediment chronosequence. The distribution of DNA markers indicated an agro-pastoral practice transition from pasture/crop production to forested landscape from the second Iron Age/classical Antiquity to the end of the Roman period/modernity, in correlation with microscopic markers. During the second Iron Age/classical Antiquity, hollows were likely used to water herds, whereas during the late Antiquity, low Bacteroidales abundances and the sporadic detection of bovine and ovine DNA markers confirm the progressive afforestation observed using pollen data. For the end of the Roman period and modern times, reforested areas are characterized by the absence of ovine and bovine DNA markers while low Bacteroidales abundances suggest the presence of wild herbivores. The present study has established that in tandem with microscopic methods, sediment-archived fecal-specific bacterial and mitochondrial DNA are extremely useful for reconstructing agricultural practice over timeframes of millennia.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".