Spatiotemporal data of algal blooms phenology
Bibliographic record
Abstract
The spatio-temporal data contain annual phytoplankton bloom phenology from 2000 to 2016 on 580 lakes located in the province of Quebec, Canada, between 44 and 50°N and 67 and 80°W. The data include height phenological variables related to phytoplankton blooms, and 17 physiographical, morphological, and climatic descriptors of the lake and the watershed. For each studied year, phenological variables were established as follows: (1) the frequency (the number of days when Chl-a concentrations remained above the threshold), (2) the intensity (the maximum concentration of Chl-a detected during a bloom), (3) the relative area (the maximum area occupied by a bloom normalized by the lake area), (4) the onset date and (5) the end date (respectively, the first and last day of the year when a bloom was detected), and (6) the duration (the number of days between the onset date and the end date). Remote sensing determination of the end date and duration of blooms is challenging because the studied region is frequently covered by clouds during the fall, significantly reducing the number of MODIS images available for this period. This is especially true during the month of October, for which there was on average half as many MODIS images without full cloud cover than between May and September. The variables describe what can be considered as an annual-based phenology, compiling days with less than 25% cloud cover and for which remotely sensed Chl-a was above the established threshold, for any given pixel. A geo-referenced database of 17 morphological, physiographic, and climatic characteristics of the watershed of each studied lake was established. The boundaries and morphological descriptors of the watersheds (area, slope) were calculated from the Canadian Digital Elevation Model with a spatial resolution of approximately 30 m. Climate data were calculated from North American Regional Reanalyses with a spatial resolution of approximately 32 km. The cumulative degree-days (°C day) was calculated by summing the recorded degrees (°C) each day above 20°C, considered as a threshold for cyanobacterial growth. Even though the remote sensing approach used here is not specific to cyanobacterial biomass, this climate proxy is considered valid since algal growth in general is stimulated by warm waters. Land use data (at 40 m spatial resolution) as well as agricultural and ecumene data (at 25 m spatial resolution) were provided by Natural Resources Canada. The environmental indicators were considered stationary over the period 2000–2016.
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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 | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".