Anthropogenic activity in the Halifax region, Nova Scotia, Canada, as recorded by bulk geochemistry of lake sediments
Bibliographic record
Abstract
Dunnington DW, Spooner IS, Krkošek WH, Gagnon GA, Cornett RJ, Kurek J, White CE, Misiuk B, Tymstra D. 2018. Anthropogenic activity in the Halifax region, Nova Scotia, Canada, as recorded by bulk geochemistry of lake sediments. Lake Reserv Manage. 34:334–348. Separating the timing and effects of multiple watershed disturbances is critical to a comprehensive understanding of lakes, which is required to effectively manage lacustrine systems that may be experiencing adverse water quality changes. Advances in X-ray fluorescence (XRF) technology has led to the availability of high-resolution, high-quality bulk geochemical data for aquatic sediments, which in combination with carbon, nitrogen, δ13C, and δ15N have the potential to identify watershed-scale disturbance in lake sediment cores. We integrated documented anthropogenic disturbances and changes in bulk geochemical parameters at 8 lakes within the Halifax Regional Municipality (HRM), Nova Scotia, Canada, 6 of which serve as drinking water sources. These data reflect more than 2 centuries of anthropogenic disturbance in the HRM that included deforestation, urbanization and related development, and water-level change. Deforestation activity was documented at Lake Major and Pockwock Lake by large increases in Ti, Zr, K, and Rb (50–300%), and moderate increases in C/N (>10%). Urbanization was resolved at Lake Fletcher, Lake Lemont, and First Lake by increases in Ti, Zr, K, and Rb (10–300%), decreases in C/N (>10%), and increases in δ15N (>2.0‰). These data broadly agree with previous paleolimnological bioproxy data, in some cases identifying disturbances that were not previously identified. Collectively these data suggest that bulk geochemical parameters and lake sediment archives are a useful method for lake managers to identify causal mechanisms for possible water quality changes resulting from watershed-scale disturbance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".