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Record W6926168753 · doi:10.21966/yk87-4x24

High-resolution record of sea surface nitrate at Sentry Shoal in the Northern Strait of Georgia, British Columbia, Canada from 2015 to 2017

2019· dataset· en· W6926168753 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHakai Institute · 2019
Typedataset
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsSpring (device)ShoalMooringBiogeochemical cycleShoreNitrateSalinity

Abstract

fetched live from OpenAlex

The Environment Canada weather mooring at Sentry Shoal in the Northern Strait of Georgia provides a platform of opportunity for collection of high frequency biogeochemical measurements. From spring to fall in 2015, 2016, and 2017, autonomous sensors were deployed on the mooring to collect surface nitrate, temperature and salinity measurements every 30 minutes. Nitrate concentration was measured using a Satlantic SUNA (Ultraviolet Nitrate Analyzer), temperature and salinity were measured using a SeaBird 37-SMP MicroCAT. Sensors were deployed each year in the spring and serviced in the field every 2-3 months, following recovery in the fall, sensors were shipped for factory service and calibration. This project was supported by the Tula Foundation and the Pacific Salmon Foundation, fieldwork was a collaborative effort between the Hakai Institute, and SeaThis Consulting.

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 score0.441
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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