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Record W6949777341 · doi:10.5281/zenodo.3758548

Astronomy in a Low-Carbon Future

2019· article· en· W6949777341 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicConferences and Exhibitions Management
Canadian institutionsUniversity of WaterlooHerzberg Institute of AstrophysicsMcGill University
Fundersnot available
KeywordsIncentiveGreenhouse gasVettingCuriosityClimate changeScholarshipSustainability

Abstract

fetched live from OpenAlex

The consequences of climate change are developing into a vast and unparalleled crisis, which is planetary in scale and yet very human in its causes and impacts. Faced with this truth, inaction and delay are neither ethical nor wise: every field of human activity, astronomy included, must take urgent steps to mitigate the crisis and avoid the worst potential outcomes, while adapting to those that are inevitable. We outline a strategy for climate-responsible scholarship and communication that we hope will resonate with the astronomical community's aspirations for a stable future in which there is room for curiosity and discovery.<br> <br> In light of the crisis, greenhouse gas emissions must be understood as significant research costs to be justified, budgeted, and rationed. Air travel is a major contributor. We must remove incentives in tenure and promotions for profligate travel, codify low-carbon practices, and reward efforts to minimize emissions while maximizing research. Any essential travel emissions must be fully offset, with carbon offsets that are reimbursable if not covered by the institution. <br> <br> The frequency, timing, and locations of conferences and colloquia should be planned to minimize air travel, with remote participation encouraged using online platforms. Conferences should favour lowest-impact food options by default, taking steps to minimize food waste (and other waste as well).<br> <br> Infrastructure must be planned with climate impacts in mind for both construction and operation. For buildings, this means pushing for designs and materials at the forefront of sustainable technology. For observatories and computing facilities, it includes justifying and thoroughly vetting the emissions footprint at the proposal stage and fully considering low-impact options, such as cloud computing.<br> <br> In education and outreach, we must strive to be reliable sources of scientific information on climate change, prepared to connect this to astrophysics and ready to direct curious people to authoritative sources.<br> <br> We recommended that CASCA support this transformation by establishing a sustainability committee (or office) to be tasked with conducting a community-wide survey of our greenhouse gas emissions and developing a decarbonization road map for the discipline. This committee would host sustainability forums and provide resources to support each of our other recommendations, especially climate-related materials and references for education and outreach.<br> <br> We recommend that CASCA push for climate-responsible governance. Governments, granting agencies, and universities must implement climate-responsible policies, like rationing the carbon budget of the entire discipline, using climate impacts to evaluate proposals, permitting the use of funds for offsets, and allowing climate-friendly but more costly travel options. Moreover, these entities must ultimately ration emissions to ensure that targets are met.<br> <br> To have an impact beyond the bounds of professional astronomy, we encourage astronomers to press their institutions to divest from fossil fuel extraction and its financing.<br> <br> Finally, we recommend that climate responsibility be an explicit priority in the 2020 Long Range Plan, and that it be used to guide all aspects of our profession, from our research and education practices to our infrastructure.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0210.005

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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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
Published2019
Admission routes1
Has abstractyes

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