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Record W6889702942 · doi:10.25976/vmet-ct64

LakeWatch Water Quality Data

2022· dataset· en· W6889702942 on OpenAlexaboutno aff

Bibliographic record

VenueDataStream · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualitySampling (signal processing)Citizen scienceWatershedTRIPS architectureSample (material)Government (linguistics)Hydrology (agriculture)

Abstract

fetched live from OpenAlex

The Alberta Lake Management Society accepts requests from citizen scientists across Alberta to have their lake monitored as part of the LakeWatch program. Volunteers often contact the Alberta Lake Management Society due to concerns around eutrophication, harmful algal blooms, watershed developments, biodiversity monitoring, and for the early detection of aquatic invasive species. If accepted into the program, a lake will be monitored 4-5 times throughout the open water season: once in June, once in July, twice in August, and once in September. ALMS hires and trains field technicians in proper sampling techniques and it is these field technicians who arrange the sampling trips with the citizen scientists. At the lake, the citizen scientist’s role is to transport the technicians around the lake on a boat and to assist with sampling. The field technicians provide all necessary sampling equipment, support the volunteers in training, and oversee sample preservation, handling, and shipment. This program is free of charge for individuals hoping to collect water quality data from their lake. This program is made possible with the support of various funders, including the Government of Alberta, and would not be possible without hundreds of hours of volunteer time by lake stewards.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0110.021
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.4140.072

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 head0.133
GPT teacher head0.387
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2022
Admission routes1
Has abstractyes

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