Bibliographic record
Abstract
Overall, there were 908 commits that closed 217 issues, together with 37 pull requests since our last release on 2023-07-31. Changes by Package libpysal #629: remove xarray as hard dependency #585: CI against Python 3.12 #627: pass kwargs to explore #617: ENH: explore method for graph #625: fix fileio regression #624: regression in libpysal.io.fileio.FileIO #622: lint & format – io/geotable/* #616: fix index check for precomputed graph distances #620: lint & format io/*.py #621: TST: ignore pandas dev blockmanager warnings #619: lint & format graph/tests/*.py #618: lint graph/_*.py #615: lint graph/base.py #614: format & lint – cg/tests/*.py #593: ENH: add vectorized plotting to Graph #612: proper shapely being pulled into DEV CI? #611: format & lint – cg/ops/tests*.py #613: Print spatial versions in CI #610: format, lint, numpydoc – cg/ops/*.py #608: ./cg/*.py – format, lint, & remove docstring spaces #607: lint ./examples/* #604: remove redundant .coveragerc #602: remove .coveragerc #605: failures due to removals of in libpysal.common #601: TST: convert unittests to pytest #595: Convert all testing from unittest to pytest. #600: remove test_NameSpace.py #596: get rid of test_NameSpace.py? #597: begin the repo linting #598: convert README to markdown #330: README.md? #592: MAINT: dealing with sqlalchemy & geomet #588: sqlalchemy and geomet #591: MAINT: avoid most of the warnings coming from dev CI #582: GeoPandas FutureWarning in fuzzy_contiguity #465: vectorize centroid in W.plot #464: W.plot method is generating long list of deprecation warnings #587: TST: fix alpha_shape failures on dev #586: avoid could_be_isomorphic on nx 3.2 #584: manual trigger for CI #583: Delay example data dir creation, add fallback for unwriteable $HOME #565: remove conftest.py in graph tests #530: is geomet still a dependency? #581: README is rst, not md --> update pyproject.toml #580: Update release action, etc. to prep for v4.8.0 #579: overhaul infrastructure #578: GHA: update release action #577: implement higher_order, components via sparse.matrix #576: TST: properly skip isomorphic test #575: implement set ops and to_networkx #574: triangulation tests + better ci setup #571: finalise kernel constructors in graph access esda #272: Adjust imports to updates in libpysal #262: release #271: Access packages directly, not through libpysal.common #270: Revert 22 dependabot/GitHub actions/codecov/codecov action 4 #269: update codecov version GHA #260: Moment of area correctness issues #267: Bump actions/checkout from 3 to 4 #266: force bash across OS #265: GHA: update release actions #264: fix math formatting in second_areal_moment #261: fix second areal moment calculation, cascade changes down to other stats #263: TST: skip tests failing on sklearn regression #254: adbscan test failures #253: add first draft of correlogram #256: update to pyproject #258: CI: properly test min and dev #239: Geary failing in CI; possibly due to libpysal#510 #257: Use more precise calculation of minimum bounding circle area giddy #205: fix small errors in README #204: [pre-commit.ci] pre-commit autoupdate #200: swap out setup.py for pyproject.toml #203: Migrate to pyproject.toml #202: Lint repo with ruff #201: black format repo #179: add gha workflow for publishing docs #199: Implement GHA for building docs #195: notebook links are returning 404 #191: update supported for Python version #197: update CI --> supported Python versions #196: address CI testing failures #193: How to use spatial_markov model by adopting my own data #194: how use my own data inequality #22: RTD --> GHP #45: v1.0.1 release #62: Update infra and actions (2023-10) #55: update infra – pyproject.toml, setuptools_scm, ruff, etc. #61: [pre-commit.ci] pre-commit autoupdate #60: Update requirements.txt – no numpy>=1.3 #59: [pre-commit.ci] pre-commit autoupdate #58: Update Versioneer pointpats #127: readme in pyproject #126: remove stale doc deps #121: Add symmetric st-neighbors in local knox #123: plot density on axis #124: CI: update actions #118: ENH: plot_density for KDE plotting of point patterns based on statsmodels #120: Revert "Edges" #111: Knox enhancements #119: Edges #117: migrate to pyproject #105: Fix support truncation and remove superfluous content #116: ENH: return axis when plotting QStatistic, default to equal aspect when plotting PointPattern #115: silence cg warnings #112: argument of type 'builtin_function_or_method' is not iterable segregation #219: changelog #220: update release workflow #221: rm rvlib #74: create Compute_At like summmarizing segregation function spaghetti mgwr #140: Fixed typo in covariate names #138: Joblib update to default -1 #137: Parallelization: switch from multiprocessing to joblib #136: Switching from multiprocessing to joblib #135: Can't upload the Data File #130: allow custom variable names in summary #126: Miss Built Distribution in Pypi #127: About bandwidth selection of large samples #133: Information about the effect of the else independent variable on the dependent variable #131: permissions for the steering council #128: CI: make CI run momepy #518: RLS: add a changelog for 0.7.0 #517: MAINT: update required versions, adapt CI, lint for new target Python #516: GHA: update actions #515: DOC: try fixing rtd #513: RTD failing #514: momepy.get_network_id doesnt seem to work. #512: [pre-commit.ci] pre-commit autoupdate #510: ENH: add FaceArtifacts #509: TST: adapt tests to GEOS 3.12 #508: TST: update for GEOS 3.12 #507: ENH: support single-part multipolygons in Squareness and CentroidCorners spglm #46: docs/conf.pyadjustments #45: Modernize infrastructure #44: add a codecov config file #43: Modernize infrastructure #42: migrate docs to GH Pages? #34: numpy.float deprecated #40: Lint with ruff #39: Format repo with black #36: initial SPGLM modernization #15: unittests in inline docstrings? #35: swap from master to main branch spint spreg #129: adapt imports to update in libpysal.common #128: failures due to update in libpysal.common #103: Add Wilkinson formula interface and scikit-learn style estimators #115: add Python 3.11 tests #124: Update pyproject.toml – numpy version requirement #116: add panel diagnostics to API docs #121: modernize infrastructure #122: Bump actions/checkout from 2 to 4 #123: Bump actions/cache from 2 to 3 #118: Updates for spreg 1.4 #120: Revert "Bump codecov/codecov-action from 3 to 4" #119: Bump codecov/codecov-action from 3 to 4 #117: Bump actions/checkout from 3 to 4 spvcm tobler #188: BUG: fix misalignment of Series in pycno #189: astropy not always needed in pycno #186: keep index #183: move dask to function-level import #180: [WIP] Area-weighted interpolation in Dask #179: update release workflow mapclassify #198: [pre-commit.ci] pre-commit autoupdate #197: remove numba cache from fisher jenks #196: remove 2 more superfluous files #195: lint code base with ruff #185: Fix linting failures #184: [pre-commit.ci] pre-commit autoupdate #193: infrastructure modernization, etc. #192: minimal requirements CI env #190: modernize infrastructure (2023/09) #194: remove usage of geopandas.datasets #191: testing with geopandas.dataset module – deprecation #189: Bump actions/checkout from 3 to 4 #188: CI: test against nightlies #187: test against the nightly wheel – numpy #186: numpy 2.0 support #183: Update Versioneer splot spopt #414: v0.6.0 release checklist #431: K-nearest p-median demo notebook #401: no demo for k-nearest p-median #410: client demand and facility capacity is transposed in LSCP.from_geodataframe() #430: fix Church 2018 (tobler paper) cite #426: Review messaging in KN-PMP #425: KN-PMP – review warnings & error messages #424: parameters & docstring clean up in KNearestPMedian #423: clean up KNearestPMedian parameters & docstring #420: Formulation of KN-PMP omitted opening constraints #429: flesh out kn-pmp testing #427: fill out testing for k-nearest p-median #419: no opening constraints in $k$-nearest $p$-median #422: locate models – standardized default names #421: standardize default model name attributes in locate #418: stricter linting – follow up #413 #417: Prepend capacitated model name #416: Inconsistent "capacitated" model name label #413: Maint work for update supported Python versions, CI environments, et.c #412: update supported Python versions & CI environments #364: Consider using pulp.Binary rather than pulp.Integer as the type for cli_assgn_vars? #366: default to binary assignment for clients #411: solve b410 #386: TestAZP.test_azp_basic_from_w CI failure #406: Fix CI failure due to AZP multiple valid MST #409: AZP Simulated Annealing #408: Update 311-DEV.yaml #407: add esda bleeding edge to DEV testing #405: Update GHA, etc (2023-10-15) #404: update GHA & CI #403: remove zip(strict=True) in k-nearest p-median #402: k-nearest pmedian failures due to zip(strict=True) #400: unexpected keyword argument 'facility_capacities' in the p-median notebook #399: Add KNearestPMP to API and documentation #381: Add a capacity p-median example #387: Add the capacity p-median example to the notebook #397: Add the k nearest p-median module and the tutorial example for capacitated p-median #398: [pre-commit.ci] pre-commit autoupdate #396: PULP_CBC_CMD not working with P-Median/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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.261 | 0.360 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".