Arboreal lichens in natural and managed high elevation spruce-fir forests of the North Thompson Valley, British Columbia
Bibliographic record
Abstract
New iterative methods for analysis of potentiometric titration data of (a) mixtures of weak monoprotic acids with their conjugate bases, (b) solutions of polyprotic (di- and triprotic) acids, and (c) mixtures of two diprotic acids are presented. These methods, using data exclusively resulting from the acidic region of the titration curve permits the accurate determination of the analytical concentration of one or more acids even if the titration is stopped well before the end point of the titration. For the titration of a solution containing a conjugate acid/base pair, the proposed procedure enables the extraction of the initial composition of the mixture, as well as the dissociation constant of the concerned acid. Thus, it is possible by this type of analysis to distinguish whether a weak acid has been contaminated by a strong base and define the extent of the contamination. On the other hand, for the titration of polyprotic acids, the proposed approach enables the extraction of the accurate values of the equivalence volume and the dissociation constants K(i) even when the ionization stages overlap. Finally, for the titration of a mixture of two diprotic acids the proposed procedure enables the determination of the composition of the mixture even if the sum of the concentrations of the acids is not known. This method can be used in the analysis of solutions containing two diastereoisomeric forms of a weak diprotic acid. The test of the proposed procedures by means of ideal and Monte Carlo simulated data revealed that these methods are fairly applicable even when the titration data are considerably obscured by 'noise' or contain an important systematic error. The proposed procedures were also successfully applied to experimental titration data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".