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Record W1565712772 · doi:10.5063/f1794348

Zooplankton in Offshore Lake Ontario during Intensive Sampling Years 2003 and 2008: Results from the LOLA (Lake Ontario Lower foodweb Assessment) program

2022· dataset· en· W1565712772 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueeCommons (Cornell University) · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsZooplanktonSampling (signal processing)OceanographyEnvironmental scienceSubmarine pipelineGeographyFisheryEcologyBiologyGeologyEngineering

Abstract

fetched live from OpenAlex

Intensive sampling of the offshore waters of Lake Ontario occurs on a five-year cycle. The 2003 and 2008 binational sampling program is known as the Lake Ontario Lower foodweb Assessment (LOLA). Research cruises were conducted in spring (April), summer (July or August) and fall (September) along several north-south transects. This data package includes epilimnetic (surface layer) and whole water column measurements for zooplankton size (mm), density (#/m3) and biomass (mg dry weight/m3) by species for each site. Veliger size and density from these tows are included as a separate file. Other trophic indicators measured during this program include nutrients (total phosphorus, dissolved silica), water clarity (secchi depth), dissolved oxygen, chlorophyll a, and an assessment of phytoplankton, the microbial food web and the benthic community.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.837
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.000

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 head0.024
GPT teacher head0.205
Teacher spread0.182 · 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