MétaCan
Menu
Back to cohort
Record W4382178363 · doi:10.5281/zenodo.8087238

Survey Planning, Allocation, Costing and Evaluation (SPACE) Project: Developing a Tool to Help Archaeologists Conduct More Effective Surveys

2023· paratext· en· W4382178363 on OpenAlexaff
Edward B. Banning, Steven Edwards, Isaac Ullah

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeparatext
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsNova Scotia Community CollegeUniversity of Toronto
Fundersnot available
KeywordsActivity-based costingSpace (punctuation)Computer scienceEngineering managementOperations researchBusinessEngineeringMarketing

Abstract

fetched live from OpenAlex

Designing an effective archaeological survey can be complicated and confidence that it was effective requires post-survey evaluation. The goal of SPACE is to develop software to facilitate survey designers’ decisions and partially automate tools that depend on mathematical models so that archaeologists can conduct surveys that accomplish their goals and evaluate their results more easily. We aim for SPACE to be a modular and accessible web-based platform for survey planning and quality assurance, with a “front end” that has a non-threatening, question-and-answer format. Its several interacting modules will ultimately include ones for evaluating visibility, estimating sweep widths and coverage, costing, determining sample sizes, transect and test-pit intervals, allocating effort optimally for stratified samples and predictive surveys, and quality assurance. In this paper, we focus on the module for estimating fieldwalkers’ sweep widths on the basis of “calibrations” on fields seeded with artifacts, while also reviewing the overall structure of the project. Sweep widths are critical for estimating coverage, evaluating survey effectiveness and quality, and planning transect intervals.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.021

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 head0.192
GPT teacher head0.350
Teacher spread0.158 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicWater Quality and Resources StudiesFrench-language works237,207