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Record W4417240732 · doi:10.1007/s44353-025-00070-y

Freshwater management informed through paleolimnology in Halifax regional Municipality, Nova Scotia, Canada

2025· article· en· W4417240732 on OpenAlexafffundabout
Kathleen Hipwell, Allison Elizabeth Covert, Andrew S. Medeiros

Bibliographic record

VenueDiscover Conservation · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPaleolimnologyNova scotiaNova (rocket)Limnology

Abstract

fetched live from OpenAlex

Watershed-scale stress from urbanization can negatively impact freshwater ecosystems and the services they provide, but our ability to manage these systems is limited by a lack of baseline knowledge. Halifax Regional Municipality (HRM), Nova Scotia, Canada, has the 8th fastest growing metropolitan area in Canada; hundreds of lakes are increasingly influenced by development, and most lakes in the urban-suburban core are developed to some degree. Lake monitoring in HRM has shown increased productivity in developed watersheds; however, a lack of historical context impedes lake management. Here, we establish a historical timeline through the analysis of biological indicators (subfossil chironomids) and elemental and isotopic geochemical records in a paleolimnological approach applied to three HRM lakes. We found that two lakes in residentially developed watersheds experienced a shift in chironomid taxa towards those indicative of human impact ( Chironomus , Cladotanytarsus mancus -type); however, our analysis show that much of the changes observed to each lake occurred prior to recent housing development. Municipal water quality monitoring programs are limited to the last four decades, and conclusions about development resulting in eutrophication contrast with the findings of this study, where we found impacts associated with land clearance and modification of habitat pre-1900. This highlights the limitations of poor resolution “snapshot” surface water quality monitoring compared to paleolimnological methods, which can capture high resolution conditions within a lake. Our results will enable informed decisions related to restoration/remediation targets and assist in the planning and management of freshwater resources.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.278
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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