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Record W4409937082 · doi:10.5040/9781472862747

Jagdpanther vs 17-pdr Achilles

2025· book· en· W4409937082 on OpenAlexaboutno aff
Frank Baldwin

Bibliographic record

VenueBloomsbury Publishing Plc eBooks · 2025
Typebook
Languageen
FieldMedicine
TopicMedical and Health Sciences Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

<JATS1:p>This is the story of the 17-pdr Achilles and the Jagdpanther, two formidable tracked anti-tank guns that clashed in North-West Europe during 1944–45.</JATS1:p> <JATS1:p>Both mounting their country’s most effective anti-tank ordnance on a tracked chassis, the 17-pdr Achilles and the Jagdpanther were arguably the best self-propelled anti-tank guns used by the British, Canadian and German forces that fought in North-West Europe during 1944–45. Featuring specially commissioned artwork and carefully chosen photographs, this is the story of the two types’ development, combat use and legacy in the closing stages of World War II in North-West Europe.</JATS1:p> <JATS1:p>Based upon the mobile, lightly armoured M10 design originally developed for the US Army, the Achilles had its main armament, the 17-pounder QF anti-tank gun, mounted in a fully revolving turret. Conversely, the low-profile, heavily armoured Jagdpanther had its formidable 8.8cm PaK 43 cannon mounted in a fixed casemate.</JATS1:p> <JATS1:p>Both crewed by artillerymen rather than tankers, the Achilles and the Jagdpanther were anti-tank guns, not tanks or assault guns; their main purpose was to knock out enemy tanks, not to engage infantry or lead an assault or pursuit. Sometimes they faced each other, notably in the Reichswald fighting of February 1945. Fully illustrated, this work tells the story of their development and tactical use as well as what happened when these two very different designs met in combat.</JATS1:p>

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.335
Teacher spread0.286 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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