The Canadian Geo-location Endeavour Using Isotopes and Trace Elements in Hair
Bibliographic record
Abstract
samples. To address objective 1, a cross-Canada sampling campaign has been started to collect hair and tap water samples. In the past two years, our group has collected samples from the eastern part of Canada (Newfoundland, Nova Scotia, New Brunswick, Prince Edward Island, Quebec and Ontario). Water samples are divided into three groups ‐ groundwater, surface water and bottled water. The GIS maps show the isotopic distribution of the tap water sources varies with latitude. Hair is analyzed for carbon (C), nitrogen (N) and hydrogen (H) isotopes. The C and N results show that in general, Canadians eat a typical diet showing a small isotopic variation. However, some cases will be presented which may explain why some people have C and N values outlying the collected sample average. In terms of H isotopes in human hair, GIS maps illustrate the distribution of this isotope in the eastern provinces of Canada. In some cases, a large variation in H was observed for the same locality with no significant difference in human activities and/or consumption. However, based on hair collected from across Canada from previous years, H isotopes in hair show a correlation to water collected from the same locality. To address objective 2, hair and tap water samples were collected at 4 month intervals (to represent different seasons in Canada) from several volunteers residing in two cities located in the province of Ontario (i.e. Sudbury and Ottawa) and one city from the province of Quebec (i.e. Montreal). For all isotopes measured, there was little variation observed over the course of the year in any individual from those small to medium-size cities. On-going sampling efforts will address if any variation may occur on a yearly basis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".