MétaCan
Menu
Back to cohort
Record W2039149400 · doi:10.5194/bgd-6-4587-2009

Seasonal pH and aragonite saturation horizons in the Gulf of Alaska during the North Pacific Survey, 1956–1957

2009· article· en· W2039149400 on OpenAlexaffabout
Skip McKinnell, James R. Christian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of VictoriaFisheries and Oceans CanadaNorth Pacific Marine Science Organization
Fundersnot available
KeywordsAragoniteOceanographyHydrographySaturation (graph theory)LatitudeGeologyHydrographic surveyCarbonateClimatologyEnvironmental scienceGeographyMineralogyCalciteChemistry

Abstract

fetched live from OpenAlex

Abstract. The extent of global change in carbon system parameters can only be evaluated by comparing present with past measurements. In the northern North Pacific, where aragonite saturation horizons are among the shallowest in the world, historical measurements of carbonate parameters vary from rare to nonexistent. However, during the summer of 1956 and winter of 1957, an extensive survey of the oceanography of the Northeast Pacific, under the auspices of the Canadian Committee on Oceanography, was conducted by the Fisheries Research Board of Canada. Approximately 2500 measurements of pH at depths from surface to 2000 m were taken throughout the Gulf of Alaska, in addition to measurements of nutrient and hydrographic properties. After conversion to the contemporary total pH scale, these data revealed significant seasonal and latitudinal differences in pH in the upper 200 m. Estimates of aragonite saturation indicate that undersaturated water was a common feature of the surface mixed layer north of 51° N latitude in the winter of 1957. The North Pacific Survey data were compared with the results of a summer 2007 survey of the west coast of North America where pH levels were ~0.1 pH units lower (at a reference density of 26.2σθ than was found in the summer of 1956.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2009
Admission routes2
Has abstractyes

Explore more

Same topicOcean Acidification Effects and ResponsesFrench-language works237,207