Mapping four decades of lake chlorophyll-a across a continental watershed: A dataset for the lake winnipeg basin (1984–2023)
Bibliographic record
Abstract
We present a standardized pan-watershed dataset of annual chlorophyll-a concentration (Chl-a) in 27,313 lakes (≥ 10 ha) draining into Lake Winnipeg, Canada, spanning 1984-2023. Lake polygons from HydroLAKES were integrated with Landsat 5/7/8 Collection 2 imagery processed in Google Earth Engine (GEE) using a reproducible workflow that (1) filters July-October scenes (peak phytoplankton biomass season), (2) masks non-water pixels from each scene, (3) converts Landsat digital numbers to surface reflectance values, (4) applies a cross-sensor Chl-a retrieval model calibrated against in-situ samples, (5) calculates the spatial mean of Chl-a in each lake for each scene, and (6) calculates the median value of all spatial-mean values per lake per year. Outputs include per-lake annual Chl-a provided as both natural-log and back-transformed Chl-a (µg L⁻¹) plus annual trophic state classes delivered in an Excel workbook and two geodatabases for mapping. The accompanying annotated GEE and R codes, input lake boundaries, and documentation enable transparent reuse and straightforward adaptation to other regions, time periods, or sensors. This resource fills a critical monitoring gap for an agriculturally influenced, bloom-prone continental watershed and supports research and management by establishing productivity baselines, detecting departures from historical conditions, and assessing bloom timing at scales relevant to decision-making. All data, inputs, and code are openly available via Zenodo.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.001 | 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".