Bibliographic record
Abstract
Absolutely constrained 143, 160-161 Absorption number 441 Acceptance criterion 445 Accidental errors 142, 420 Accuracy balanced 315 local measure of 298 of observations 11 optimal 313 overall 317 positional 159 relative 159, 348 specification for a project 313 specification for property (legal) survey 149, 158-159 specifications for distance 68 specifications for total station 67 standards 159 stated 540 Active Control Systems (ACS) 392 Adjusted angle 173, 215, 217, 220 azimuths 239, 240, 261 corrections 577-578 direction measurement 13, 220 displacements 565 elevations 207, 210, 212, 264, 268, 478, 533 height differences 215, 470 new observations 578 nuisance 380 orientation parameter(s) 184-185 positions 366 state vector 581 Adjustment in Cartesian system 367 compass rule 125, 154 conditional model 132-133, 459, 465, 480 Crandall's rule 126-127 criterion 120 in curvilinear system 371 external constraint 560, 563 free network 544-545, 551, 553 general model 502-503, 516 inner constraint 545, 548, 551, 559 leveling network 212, 243 in local system 373 minimal constraint 138, 140-143, 196-197, 235, 367, 544-545, 559 over-constrained 141, 143 parametric model 179, 264 of the regression problem 257 675 Understanding Least Squares Estimation and Geomatics Data Analysis, First Edition.John Olusegun Ogundare.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.734 | 0.673 |
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".