MétaCan
Menu
Back to cohort
Record W7096293469

Detection of Trends in Ice Season Characteristics of New Brunswick Rivers

2015· article· en· W7096293469 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLinear regressionHydrology (agriculture)Regression analysisPrecipitationLinear relationshipRegression
DOInot available

Abstract

fetched live from OpenAlex

Analyses were performed on hydrometric data for 13 selected hydrometric sites in New Brunswick to detect trends in ice season characteristics (earliest and latest dates of ice effects on hydrometric records, the number of days with ice effect, freeze-up and breakup flows and the estimated number of break-up events each year). The coefficient of determination, r2, and the p-value for the slope of a linear regression line were used to assess the significance of any possible trends in the data. The coefficient of determination, r2, is a measure of the degree of relationship between two variables. The slope of the regression line depicts the average rate of change in a variable over time, and its probability indicates if the slope value is statistically significantly different from zero. In the absence of more specific information, the notation of a backwater effect in the hydrometric records was used to evaluate the beginning and end of periods of ice, the number of ice-covered periods and the duration of the ice season. The existence of a backwater effect is indicated in Environment Canada’s HYDAT database by the symbol “B”. It was found that trends in hydrologic data are beginning to appear in the hydrometric records. As the climate continues to change, trends in hydrologic data are expected to be more evident and statistically significant.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.249
Teacher spread0.197 · 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.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicOral History, Memory, Narrative AnalysisFrench-language works237,207