Mid to late Holocene hydrological change in continental eastern Canada: assessing regional trends from ombrotrophic peat records.
Bibliographic record
Abstract
ABSTRACT High resolution records of mid-late Holocene hydro-climatic change are presented from Mer Bleue Bog, eastern Ontario. Past climatic changes in this region have previously been inferred from lake sediments, but rain-fed peatlands can offer additional insights into the spatial and temporal pattern of moisture availability. In this study, reconstructed water table depths are based on a testate amoeba-derived transfer function developed for the region and changes in bog surface wetness are compared with plant macrofossil and peat humification data. RÉSUMÉ Nous présentons les enregistrements hautes résolutions des variations hydrologique durant la second moitié de l’Holocène pour les tourbières Mer Bleue á l’est de l'Ontario. Précédemment, les changements climatiques de cette région ont été dérivés à partir de prélèvement de sédiments de lac. Mais ils s’avèrent que les tourbières ombrotrophes offrir un éclairage supplémentaire sur les schémas de répartition spatiale et temporelle de la disponibilité de l'humidité. Dans cette étude, des profondeurs reconstruites de nappe phréatique sont basées sur un modèle de function de transfert d’amibes (Arcellinida) et des changements de l’humidité de surface de la tourbière sont comparés avec les macrofossils et au humification de tourbe dans une analyse multi-proxy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".