Have watershed disturbances over the past ~200 years affected diatom composition in Algonquin Provincial Park lakes? A paleolimnological assessment
Bibliographic record
Abstract
Algonquin Provincial Park (APP), located in south-central Ontario, spans an area of 7,635 km2 and is a destination for over 800,000 visitors a year. These visitors include cottage owners, as cottages have been leased in APP since the early 1900s. In October 2017, policy makers at the Ontario Ministry of Resources and Forestry (MNRF) announced the renewal of cottage leases until December 2038, following a review process that included public consultation. However, the impacts of these cottages and other current and historic anthropogenic stressors on lake ecosystems are not yet well understood. To address this knowledge gap, paleolimnological methods were used to assess whether anthropogenic stressors have affected APP lakes over the past ~200 years. Using diatom assemblage composition, alongside spectrally-inferred chlorophyll a and lake-water total organic carbon (TOC) concentrations from 210Pb-dated sediment cores collected from five lakes, we determined pre-industrial (baseline) conditions and examined whether historical catchment disturbances (e.g., logging, cottages, commercial establishments), as well as regional climate change, have registered a response in the sedimentary records. To help distinguish the effect of catchment development from natural variability, comparisons were made between three lakes (Cache, Canoe, Smoke) that support cottages and commercial establishments and two remote reference lakes (McIntosh, Welcome). Additionally, Cache Lake was included in a productivity experiment in which large quantities of fertilizer were added between 1946 and 1947. In general, diatom assemblage changes were modest in all lakes, except for a clear response to the nutrient manipulations in Cache Lake. In all lakes, early 19th century diatom compositional changes together with trends in chlorophyll a and TOC appear to be a response to logging in the region. Importantly, we found little evidence that cottages have made a discernible impact on these lakes.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".