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Data accompanying Timmins-Schiffman et al. 2013

2014· article· en· W2290616638 on OpenAlexaboutno aff
Timmins-Schiffman Emma

Bibliographic record

VenueFigshare · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

These 3 files are the source data for Table 1 and Figures 3, 4, and 5 in Timmins-Schiffman et al. 2013 Elevated pCO2 causes developmental delay in early larval Pacific oysters, Crassostrea gigas, published in Marine Biology (doi 10.1007/s00227-012-2055-x). Table 1: Salinity, total alkalinity (AT), and spectrophotometric (spec) pH are point measurements taken each day. Partial pressure of CO2, carbonate saturation, and carbonate ion concentration were calculated from spec pH and AT. Mean and standard deviation (u ± SD) for the following parameters are given for all 3 days: temperature, salinity, AT, pH, pCO2, and carbonate ion concentration. Data for Fig 3: Number of larvae scored as calcified, uncalcified, or partially calcified are given for different replicates (jars) for the two time points (24 and 72 hours post-fertilization) and different pCO2 conditions (400, 700, or 1000 µatm). Data for Figs 4 and 5: Each cell represents a measurement for an individual larva. Column headers are formatted [measurement type][pCO2].[day measurement taken], i.e. height400.day1 contains data for larval shell height from the 400 uatm treatment on day 1 post-fertilization. Data are for shell height and depth, pCO2 of approximately 400, 700, and 1000 uatm, and days 1 and 3 post-fertilization. Data for Fig 6: Data were plotted for individual oysters with measurements of both shell height and hinge length. Columns have data for day post-fertilization, pCO2 (in uatm), DayTreatment combined factor, hinge length, and shell height. Each row contains data for a single oyster larva.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient 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: none
Teacher disagreement score0.522
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.2650.029

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 head0.079
GPT teacher head0.285
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

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

Citations0
Published2014
Admission routes1
Has abstractyes

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