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Record W7048175759

The Introduction of Lumbering In Lavant And Darling Townships

2024· dissertation· en· W7048175759 on OpenAlexaffabout

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNucleofectionHyporeflexiaGestational periodTSG101
DOInot available

Abstract

fetched live from OpenAlex

The significance of the Canadian timber industry and its subsequent contributions to the economic structure of early Canada is well-documented. Discussions on the growth of the industry centre around the expanding American markets, British interest in Maritime forest resources and the shifting of interest to the resources of the Canadian Shield, culminating on the trade in the Ottawa Valley. The effects of water transportation routes and construction of the railway system on trade are also significant themes. Furthermore, historical sketches on various lumbering giants, and tales of myth-like lumberjacks and shanty towns add colourful sources of slightly more specific details of the lumbering era. The relationship between the settler and lumbermen comprises yet another area of relevant concern. Studies of settlement patterns and timber boom towns logically follow the discussions on the partnership between lumbermen and farmers.To fully appreciate the significance of the timber trade in Canada, and more specifically in Upper Canada, a clear appreciation of all the above data is necessary. One level of documentation, however, is curiously absent from the discussion on lumbering and that is the level of very specific detail. Very few attempts have been made to document the arrival of lumbering on the primary level of individual land licence application. This paper acknowledges this oversight and initiates a methodological, lot by concession lot, study of lumbering in a defined study area.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.689

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)0.0010.000

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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 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
Published2024
Admission routes2
Has abstractyes

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