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Record W7071982492

Trends and variability of temperature and precipitation in Northwestern Ontario during the 20th century : implications for forest management

2017· dissertation· en· W7071982492 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationClimate changeContext (archaeology)Global warmingEcosystemRange (aeronautics)Trend analysisAir temperatureMean radiant temperatureEffects of global warming
DOInot available

Abstract

fetched live from OpenAlex

Time series of temperature and rainfall in Northwestern Ontario are joined and
\nadjusted to provide a database for analysis of regional climate change.
\nProcesses used to locate and adjust for non-climatic discontinuities are provided.
\nTemperature trends were computed for 1916-1998. All available stations have
\nincreases in mean annual temperature. The greatest warming occurred in the
\nspring at all stations with most stations > 1.0? C warmer. Some increase in
\nwinter and the growing season temperature has taken place. A slight negative
\nchange has taken place in the fall in most stations. The warming has been
\ngreater In minimum temperatures than maximums with resulting declines in the
\ndiurnal temperature range especially evident in the first half of the period.
\nAverage rainfall has increased at all stations in the region. An analysis of daily
\nrain events suggests increases in frequency and amounts of individual events
\n>40 mm in southern locations and 30.0 - 39.9 mm In the northern stations.
\nTrends and variability in these variables during the 20th century are analysed to
\ndefine a base and context for predictions of significant climate change In the 21st
\ncentury. Ecosystem change because of climate change is likely to have major
\nimpacts on forest management and wood-based products, an economic sector of
\nmajor importance in the region.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.401

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 studies0.0010.000
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 head0.018
GPT teacher head0.218
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
Published2017
Admission routes1
Has abstractyes

Explore more

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