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Record W4386580639 · doi:10.1016/j.icarus.2023.115774

Limited recharge of the southern highlands aquifer on early Mars

2023· article· en· W4386580639 on OpenAlexaff
Eric Hiatt, Mohammad Afzal Shadab, S. P. S. Gulick, T. A. Goudge, Marc A. Hesse

Bibliographic record

VenueIcarus · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Aeronautics and Space Administration
KeywordsGroundwater rechargeAquiferGeologySurface runoffDepression-focused rechargeHydrology (agriculture)Hydraulic conductivityPrecipitationMars Exploration ProgramGroundwaterEnvironmental scienceSoil scienceSoil waterGeographyMeteorologyGeotechnical engineering

Abstract

fetched live from OpenAlex

To determine plausible groundwater recharge fluxes on early Mars, we developed analytic and numerical solutions for an unconfined steady-state aquifer beneath the southern highlands. We showed that the aquifer’s mean hydraulic conductivity, K, is the primary constraint on the plausible magnitude of the mean steady recharge, r. By using geologic constraints, a mean hydraulic conductivity of K ∼10−7m/s, and varying shoreline elevations and recharge distributions, the mean recharge must be of the order of 10−2 mm/yr. Recharge for other values of K can be estimated as r ∼10−5K. Our recharge value is near the low end of previous estimates and significantly below published precipitation estimates. This suggests that, in a steady hydrologic cycle, most precipitation forms runoff as opposed to infiltrating into the subsurface. Alternatively, high rates of runoff production combined with a sufficiently slow transient aquifer response to recharge may limit major groundwater upwelling prior to the cessation of climatic excursions causing precipitation.

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)0.001
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.022
GPT teacher head0.219
Teacher spread0.197 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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