Holocene landscape change at the northern Manitoba tundra-forest border 2003-2009
Bibliographic record
Abstract
The purpose of this study was to understand how location within a landscape influences the response of aquatic and terrestrial ecosystems to climate change (with particular focus on how lake ecosystems respond to peatland development) at the tundra-forest border of northern Manitoba. In 2008-2009 we sampled water and surface sediment from 44 lakes. We collected sediment cores (3-4 meters in length) from 8 of these lakes. These cores represent 4000 - 8000 years of continuous sediment deposition. The data suggest a strong link between lake water chemistry and peatland in the modern landscape, a relatively stable mix of forest and tundra over the past 7500 years and a heterogeneous expansion of peatland beginning ~4500 years Before Present (BP). This dataset includes modern lake chemistry, diatom assemblages, and morphometry and land cover data for 44 lakes sampled primarily in 2009. Proxy data are also provided for sediment cores collected from 8 lakes. Data for the cores include Lead 210 and 14-Carbon dates, loss-on-ignition, grain size, carbon and nitrogen (concentrations and isotopes), extractable elements (Calcium, Magnesium, Iron, Potassium, Phosphorous), biogenic silica, X- Ray Fluorescence (XRF), magnetics (susceptibility, Anhysteretic Remanent Magnetization (ARM), Isothermal Remanent Magnetization (IRM)), charcoal, pollen and spores, and diatoms.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".