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Record W7099883010

WATER POWER SAWMILLS IN NEWFOUNDLAND Written and Illustrated By

2015· article· en· W7099883010 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability, Environment, and Optimization Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsMillPower (physics)GeologistIngenuityBabblingCrewIndustrial RevolutionWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

PREFACE This book is mainly the result of a family effort over the past three years. During the summer months the 'research crew ' consisted of my wife, Geraldine, our son, Stirling (who was two-and-one-half years old when we began studying old sawmills), and myself. The winter months, when we were confined to the environs of St. John's, offered time for reflection on past summer activities and opportunity for research, writing, drawing and building working models. During our research we travelled extensively throughout Newfoundland and Great Britain and, in the process, we experienced great satisfaction from these wonderful old mills of yesteryear. The above points are worth noting because it shows that it has a fascination for all ages. I suppose this book might have some merit as an industrial archaeological project- we hope so. But, as a family project, it was all fun and games. The picnics beside the babbling mill brooks amidst peaceful and beautiful forest scenery; the excitement of discovering medieval books in some dusty corner of a library; the pleasures and satisfaction in building a working model sawmill; the acquaintance of many new friends we met on our travels. But perhaps, more importantly, we experienced for ourselves that these dishevelled old mills are not merely useless brutes from the past, like some old stone relic in a glass cage that only experts can appreciate. On the contrary, mills are active places. While adults may marvel at the ingenuity of our ancestors, little children like Stirling in their fantasies see leprechauns and other fairytale heroes behind every cog and wheel; in short, to a child old watermills are great big oversized toys inhabited by all the imaginary friends their active minds can dream of. The fairytale stories of magic wee folk conjured up by a lively, excited little boy gave us the sort of precious memories that all parents treasure. Like hundreds of families, our visits to

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0620.007

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 head0.008
GPT teacher head0.210
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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